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Given a thin torus $T_K$ around a knot $K\subset \mathbb{R}^3$, we construct Morse models of cord algebra $Cord(T_K)$ with $\mathbb{Z}$ and loop space coefficients. Using the Multiple time scale dynamics we identify $Cord(T_K; \mathbb{Z})$ with $Cord(K; \mathbb{Z})$. In combination with the works of Cieliebak-Ekholm-Latschev-Ng and Petrak this indirectly relates $Cord(T_K)$ to $0$-th degree Legendrian contact homology $LCH_0(\mathcal{L}^\ast_+ T_K)$ of one component of the unit conormal bundle over $T_K$. Our definition of $Cord(T_K)$ is motivated by $J$-holomorphic curves with boundary on the Lagrangian submanifold $L^\ast_+ T_K\cup\mathbb{R}^3$ with an arboreal singularity along the torus $T_K$.
The umbilical cord plays a critical role in delivering nutrients and oxygen from the placenta to the fetus through the umbilical vein, while the two umbilical arteries carry deoxygenated blood with waste products back to the placenta. Although solute exchange in the placenta has been extensively studied, exchange within the cord tissue has not been investigated. Here, we explore the hypothesis that the coiled structure of the umbilical cord could strengthen diffusive coupling between the arteries and the vein, resulting in a functional shunt. We calculate the diffusion of solutes, such as oxygen, and heat in the umbilical cord to quantify how this shunt is affected by vascular configuration within the cord. We demonstrate that the shunt is enhanced by coiling and vessel proximity. Furthermore, our model predicts that typical vascular configurations of the human cord tend to minimise shunting, which could otherwise disrupt thermal regulation of the fetus. We also show that the exchange, amplified by coiling, can provide additional oxygen supply to the cord tissue surrounding the umbilical vessels.
Intro: Vocal cord ultrasound (VCUS) has emerged as a less invasive and better tolerated examination technique, but its accuracy is operator dependent. This research aims to apply a machine learning-assisted algorithm to automatically identify the vocal cords and distinguish normal vocal cord images from vocal cord paralysis (VCP). Methods: VCUS videos were acquired from 30 volunteers, which were split into still frames and cropped to a uniform size. Healthy and simulated VCP images were used as training data for vocal cord segmentation and VCP classification models. Results: The vocal cord segmentation model achieved a validation accuracy of 96%, while the best classification model (VIPRnet) achieved a validation accuracy of 99%. Conclusion: Machine learning-assisted analysis of VCUS shows great promise in improving diagnostic accuracy over operator-dependent human interpretation.
Large Audio Language Models (LALMs) have garnered significant research interest. Despite being built upon text-based large language models (LLMs), LALMs frequently exhibit a degradation in knowledge and reasoning capabilities. We hypothesize that this limitation stems from the failure of current training paradigms to effectively bridge the acoustic-semantic gap within the feature representation space. To address this challenge, we propose CORD, a unified alignment framework that performs online cross-modal self-distillation. Specifically, it aligns audio-conditioned reasoning with its text-conditioned counterpart within a unified model. Leveraging the text modality as an internal teacher, CORD performs multi-granularity alignment throughout the audio rollout process. At the token level, it employs on-policy reverse KL divergence with importance-aware weighting to prioritize early and semantically critical tokens. At the sequence level, CORD introduces a judge-based global reward to optimize complete reasoning trajectories via Group Relative Policy Optimization (GRPO). Empirical results across multiple benchmarks demonstrate that CORD consistently enhances audio-conditioned reasonin
Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries affecting the spinal cord. While robust, automatic segmentation methods to a wide variety of contrasts and pathologies have been developed over the past few years, whether their predictions are stable as the model is updated using new datasets has not been assessed. This is particularly important for deriving normative values from healthy participants. In this study, we present a spinal cord segmentation model trained on a multisite $(n=75)$ dataset, including 9 different MRI contrasts and several spinal cord pathologies. We also introduce a lifelong learning framework to automatically monitor the morphometric drift as the model is updated using additional datasets. The framework is triggered by an automatic GitHub Actions workflow every time a new model is created, recording the morphometric values derived from the model's predictions over time. As a real-world application of the proposed framework, we employed the spinal cord segmentation model to update a recently-introduced normative database of healthy participants containing comm
We first review the Cordes condition for nondivergence-form differential operators through the lens of Campanato's theory of near operators. We then survey a recently proposed Cordes framework that guarantees the existence and uniqueness of $L^2$ solutions to stationary Fokker--Planck--Kolmogorov equations subject to periodic boundary conditions, and that allows for the construction of a simple finite element method for its numerical approximation. Finally, we propose a Cordes framework for stationary Fokker--Planck--Kolmogorov-type equations subject to a homogeneous Dirichlet boundary condition.
Purpose: This study investigates the feasibility of transcutaneous interferential spinal cord stimulation (tISCS), a novel non-invasive neuromodulation method, using temporal interference to enhance focality and comfort in spinal cord stimulation. The central research question is whether tISCS can achieve targeted activation of spinal cord circuits while reducing unwanted stimulation of skin and muscle tissues, which are common limitations of conventional transcutaneous spinal cord stimulation (tSCS). Methods: A finite element model of the lower thorax was developed to simulate electric field distributions for various skin electrode montages. To address the computational bottleneck associated with high-resolution modeling and montage optimization, we implemented a leadfield-based Pareto optimization strategy to identify the electrode configuration that maximizes the electric field in the spinal cord and minimizes it in off-target tissues. tISCS montages were compared with tSCS montages in terms of focality and stimulation efficiency. Results: Optimized tISCS configurations significantly reduced electric field intensity in the skin by over 20-fold compared to tSCS. The ratio of spin
Objective: While FLASH radiotherapy is recognized for short-term normal tissue sparing, its durability in late-responding organs remains uncertain, limiting clinical adoption. With its clinical importance and steep dose-response, the spinal cord is an ideal model for evaluating FLASH effect on late toxicity. This work introduces a robust image-guided research platform for high-precision irradiation at both CONV and UHDR to enable FLASH late toxicity studies using a rat spinal cord model. Approach: A modified LINAC was employed to irradiate the C1-T2 rat spinal cord with 18 MeV UHDR and CONV beams. A custom rat immobilization device, a portable X-ray imaging system, and an ion-chamber-based UHDR output monitoring system were integrated to ensure accurate C1-T2 localization and precise dose delivery. A Monte Carlo (MC) dose engine was developed to provide accurate dosimetry and support interpretation of in vivo results. Scintillator measurements were performed within the spinal cord to verify MC results and the precision of our platform. Results: We achieved submillimeter C1-T2 setup accuracy and maintained submillimeter intrafraction motion. Ion chamber readings showed linear correl
Background: Trans-spinal FUS (tsFUS) has recently been shown promise in modulating spinal reflexes in rodents, opening new avenues for spinal cord interventions in motor control and pain management. However, anatomical differences between rodents and human spinal cords require careful targeting strategies and transducer design adaptations for human applications. Aim: This study aims to computationally explore the feasibility of tsFUS in the human spinal cord by leveraging the intervertebral acoustic window and vertebral lamina. Method: Acoustic simulations were performed using an anatomically detailed human spinal cord model with an adapted single-element focusing transducer (SEFT) to investigate the focality and intensity of the acoustic quantities generated within the spinal cord. Results: Sonication through the intervertebral acoustic window using an adapted transducer achieved approximately 2-fold higher intensity and up to 20% greater beam overlap compared to commercial SEFT. Precise transducer positioning was critical; a 10 mm vertical shift resulted in a reduction of target intensity by approximately 7-fold. The vertebral level also substantially influenced the sonication ou
Aims: To develop an in-silico model of the aorta and its spinal cord-supplying branches, and to characterise haemodynamic changes following aortic aneurysm (AA) repair with such a model. The work is motivated by the risk of spinal cord ischaemia (SCI) and paraplegia, serious complications that can arise from disruption of spinal cord perfusion during AA surgery. Methods: SimVascular was used to retrospectively create models of a 76 year old female patient's aorta pre- and post- uncomplicated endovascular AA repair. The full extent of the aorta and its branches, including vessels supplying the spinal cord, was segmented. Pulsatile flow simulations were conducted under the assumption of rigid vessel walls, with patient-specific inlet and three-element Windkessel models for the outlet boundary conditions on the SimVascular Gateway Cluster. Results: Postoperatively, segmental artery flow to the spinal cord decreased by 51.86% due to exclusion of lumbar and posterior intercostal arteries by the stent graft. Spinal cord-supplying arteries showed increased TAWSS (+5.2%) and reduced RRT and ECAP, with minimal change in OSI. Consistent with redistribution away from the spinal territory, mod
Objectives This study aimed to elucidate the potential mechanisms of electroacupuncture (EA) in restoring detrusor-bladder neck dyssynergesia (DBND) following suprasacral spinal cord injury. Methods A total of 52 adult female Sprague-Dawley rats were randomly assigned to either a sham group (n=12) or a spinal cord injury model group (n=40). In the model group, DBND was induced in 40 rats through Hassan Shaker spinal cord transection, with 24 rats surviving spinal shock and subsequently randomized into two groups: a model-only group (DBND, n=12) and an EA intervention group (DBND+EA, n=12). DBND+EA was administered at Ciliao (BL32), Zhongji (RN3), and Sanyinjiao (SP6) acupoints, for 20 minutes per session, once daily for 10 consecutive days. On day 29 post-injury, all rats underwent urodynamic assessments, followed by hematoxylin and eosin (HE) staining, tandem mass tag (TMT) proteomics, and Western blot (WB) analysis of the detrusor and bladder neck tissues. Results Urodynamic evaluation demonstrated that EA intervention enhanced bladder function in DBND rats. HE staining indicated reduced fibroplasia in the detrusor muscle and alleviated inflammation in the bladder neck following
Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxelwise group analyses. Traditional template-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n=267, 44 sites) and a multi-subject dataset with various neck positions (n=10, 3 sessions). We further validated the method on task-based functional MRI (n=23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared to the traditional disc-based method. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional
Spinal cord tumors significantly contribute to neurological morbidity and mortality. Precise morphometric quantification, encompassing the size, location, and type of such tumors, holds promise for optimizing treatment planning strategies. Although recent methods have demonstrated excellent performance in medical image segmentation, they primarily focus on discerning shapes with relatively large morphology such as brain tumors, ignoring the challenging problem of identifying spinal cord tumors which tend to have tiny sizes, diverse locations, and shapes. To tackle this hard problem of multiclass spinal cord tumor segmentation, we propose a new method, called BATseg, to learn a tumor surface distance field by applying our new multiclass boundary-aware loss function. To verify the effectiveness of our approach, we also introduce the first and large-scale spinal cord tumor dataset. It comprises gadolinium-enhanced T1-weighted 3D MRI scans from 653 patients and contains the four most common spinal cord tumor types: astrocytomas, ependymomas, hemangioblastomas, and spinal meningiomas. Extensive experiments on our dataset and another public kidney tumor segmentation dataset show that our
Spinal cord stimulation (SCS) electrodes are traditionally placed in the dorsal epidural space to stimulate the dorsal column fibers for pain therapy. Recently, SCS has gained attention in restoring gait. However, the motor fibers triggering locomotion are located in the ventral and lateral spinal cord. Currently, SCS electrodes are steered manually, making it difficult to navigate them to the lateral and ventral motor fibers in the spinal cord. In this work, we propose a helically micro-machined continuum robot that can bend in a helical shape when subjected to actuation tendon forces. Using a stiff outer tube and adding translational and rotational degrees of freedom, this helical continuum robot can perform follow-the-leader (FTL) motion. We propose a kinematic model to relate tendon stroke and geometric parameters of the robot's helical shape to its acquired trajectory and end-effector position. We evaluate the proposed kinematic model and the robot's FTL motion capability experimentally. The stroke-based method, which links tendon stroke values to the robot's shape, showed inaccuracies with a 19.84 mm deviation and an RMSE of 14.42 mm for 63.6 mm of robot's length bending. The
Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute fetal inflammatory response (FIR) is characterized by infiltration of the umbilical cord by fetal neutrophils, and can be associated with neonatal sepsis or fetal inflammatory response syndrome. Recent advances in deep learning in digital pathology have demonstrated favorable performance across a wide range of clinical tasks, such as diagnosis and prognosis. In this study we classified FIR from whole slide images (WSI). We digitized 4100 histological slides of umbilical cord stained with hematoxylin and eosin(H&E) and extracted placental diagnoses from the electronic health record. We build models using attention-based whole slide learning models. We compared strategies between features extracted by a model (ConvNeXtXLarge) pretrained on non-medical images (ImageNet), and one pretrained using histopathology images (UNI). We trained multiple iterations of each model and combined them into an ensemble. The predictions from the ensemble of models trained using UNI achieved an overall balanced accuracy of 0.836 on the test dataset. In comparison, the e
Background: Spinal cord injury (SCI) rehabilitation remains a major clinical challenge, with limited treatment options for functional recovery. Temporal interference (TI) electrical stimulation has emerged as a promising non-invasive neuromodulation technique capable of delivering deep and targeted stimulation. However, the application of TI stimulation in SCI rehabilitation remains largely unexplored. Methods: This study aims to investigate the feasibility of applying non-invasive TI electrical stimulation for SCI rehabilitation. Through computational modeling, we analyzed the electric field distribution characteristics in the spinal cord under different TI stimulation configurations. Based on these findings, we propose a clinically applicable TI stimulation protocol for SCI rehabilitation. Results: The results demonstrate that TI stimulation can effectively deliver focused electric fields to targeted spinal cord segments while maintaining non-invasiveness. The electric field intensity varied depending on individual anatomical differences, highlighting the need for personalized stimulation parameters. The proposed protocol provides a practical framework for applying TI stimulation
Cooperative multi-agent reinforcement learning (MARL) aims to develop agents that can collaborate effectively. However, most cooperative MARL methods overfit training agents, making learned policies not generalize well to unseen collaborators, which is a critical issue for real-world deployment. Some methods attempt to address the generalization problem but require prior knowledge or predefined policies of new teammates, limiting real-world applications. To this end, we propose a hierarchical MARL approach to enable generalizable cooperation via role diversity, namely CORD. CORD's high-level controller assigns roles to low-level agents by maximizing the role entropy with constraints. We show this constrained objective can be decomposed into causal influence in role that enables reasonable role assignment, and role heterogeneity that yields coherent, non-redundant role clusters. Evaluated on a variety of cooperative multi-agent tasks, CORD achieves better performance than baselines, especially in generalization tests. Ablation studies further demonstrate the efficacy of the constrained objective in generalizable cooperation.
This research proposal discusses two challenges in the field of medical image analysis: the multi-parametric investigation on microstructural and macrostructural characteristics of the cervical spinal cord and deep learning-based medical image segmentation. First, we conduct a thorough analysis of the cervical spinal cord within a healthy population. Unlike most previous studies, which required medical professionals to perform functional examinations using metrics like the modified Japanese Orthopaedic Association (mJOA) score or the American Spinal Injury Association (ASIA) impairment scale, this research focuses solely on Magnetic Resonance (MR) images of the cervical spinal cord. Second, we employ cutting-edge deep learning-based segmentation methods to achieve highly accurate macrostructural measurements from MR images. To this end, we propose an enhanced UNet-like Transformer-based framework with attentive skip connections. This paper reports on the problem domain, proposed solutions, current status of research, and expected contributions.
We introduce new dataset 'CORD-19-Vaccination' to cater to scientists specifically looking into COVID-19 vaccine-related research. This dataset is extracted from CORD-19 dataset [Wang et al., 2020] and augmented with new columns for language detail, author demography, keywords, and topic per paper. Facebook's fastText model is used to identify languages [Joulin et al., 2016]. To establish author demography (author affiliation, lab/institution location, and lab/institution country columns) we processed the JSON file for each paper and then further enhanced using Google's search API to determine country values. 'Yake' was used to extract keywords from the title, abstract, and body of each paper and the LDA (Latent Dirichlet Allocation) algorithm was used to add topic information [Campos et al., 2020, 2018a,b]. To evaluate the dataset, we demonstrate a question-answering task like the one used in the CORD-19 Kaggle challenge [Goldbloom et al., 2022]. For further evaluation, sequential sentence classification was performed on each paper's abstract using the model from Dernoncourt et al. [2016]. We partially hand annotated the training dataset and used a pre-trained BERT-PubMed layer. '
Red blood cells (RBCs) from the cord blood of newborn infants have distinctive functions for fetal and infant development. To systematically investigate the biophysical characteristics of individual cord RBCs in newborn infants, a comparative study was performed of RBCs from cord blood of newborn infants, and of adult RBCs from mothers or non-pregnant women, employing optical holographic micro-tomography. Optical measurements of 3-D refractive index distributions, and of dynamic membrane fluctuations of individual RBCs, enabled retrieval of the morphological, biochemical, and mechanical properties of cord, maternal, and adult RBCs at the individual cell level. The volume and surface area of the cord RBCs were significant larger than those of RBCs from non-pregnant women, and cord RBCs have more flattened shapes than RBCs in adults. In addition, the Hb content in the cord RBCs of newborns was significantly greater. The Hb concentration in cord RBCs was higher than for non-pregnant women or maternal RBCs, but they were within the physiological range of adults. Interestingly, the amplitude of dynamic membrane fluctuations in cord RBCs were comparable to those in non-pregnant women and