Dynamics of Center of Pressure Trajectory in Gait: Unilateral Transfemoral Amputees Versus Non-Disabled Individuals.

The primary goal of rehabilitation for individuals with lower limb amputation, particularly those with unilateral transfemoral amputation (uTFA), is to restore their ability to walk independently. Effective control of the center of pressure (COP) during gait is vital for maintaining balance and stability, yet it poses a significant challenge for individuals with uTFA. This study aims to study the COP during gait in individuals with uTFA and elucidate their unique compensatory strategies. This study involved 12 uTFA participants and age-matched non-disabled controls, with gait and COP trajectory data collected using an instrumented treadmill. Gait and COP parameters between the control limb (CL), prosthetic limb (PL), and intact limb (IL) were compared. Notably, the mediolateral displacement of COP in PL exhibited significant lateral displacement compared to the CL from 30% to 60% of the stance. In 20% to 45% of the stance, the COP forward speed of PL was significantly higher than that of the IL. Furthermore, during the initial 20% of the stance, the vertical ground reaction force of PL was significantly lower than that of IL. Additionally, individuals with uTFA exhibited a distinct gait pattern with altered duration of loading response, single limb support, pre-swing and swing phases, and step time. These findings indicate the adaptability of individuals with uTFA in weight transfer, balance control, and pressure distribution on gait stability. In conclusion, this study provides valuable insights into the unique gait dynamics and balance strategies of uTFA patients, highlighting the importance of optimizing prosthetic design, alignment procedures, and rehabilitation programs to enhance gait patterns and reduce the risk of injuries due to compensatory movements.

Transfemoral prosthetic simulators versus amputees: ground reaction forces and spatio-temporal parameters in gait.

This study aimed to compare the ground reaction forces (GRFs) and spatio-temporal parameters as well as their asymmetry ratios in gait between individuals wearing a transfemoral prosthetic simulator (TFSim) and individuals with unilateral transfemoral amputation (TFAmp) across a range of walking speeds (2.0-5.5 km h-1). The study recruited 10 non-disabled individuals using TFSim and 10 individuals with unilateral TFAmp using a transfemoral prosthesis. Data were collected using an instrumented treadmill with built-in force plates, and subsequently, the GRFs and spatio-temporal parameters, as well as their asymmetry ratios, were analysed. When comparing the TFSim and TFAmp groups, no significant differences were found among the gait parameters and asymmetry ratios of all tested metrics except the vertical GRFs. The TFSim may not realistically reproduce the vertical GRFs during the weight acceptance and push-off phases. The structural and functional variations in prosthetic limbs and components between the TFSim and TFAmp groups may be primary contributors to the difference in the vertical GRFs. These results suggest that TFSim might be able to emulate the gait of individuals with TFAmp regarding the majority of spatio-temporal and GRF parameters. However, the vertical GRFs of TFSim should be interpreted with caution.

Coordination of Lower Limb During Gait in Individuals With Unilateral Transfemoral Amputation.

Understanding the lower-limb coordination of individuals with unilateral transfemoral amputation (uTFA) while walking is essential to understand their gait mechanisms. Continuous relative phase (CRP) analysis provides insights into gait coordination patterns of the neuromusculoskeletal system based on movement kinematics. Fourteen individuals with uTFA and their age-matched non-disabled individuals participated in this study. Kinematic data of the lower limbs of the participants were collected during walking. The joint angles, segment angles, and CRP values of the thigh-shank and shank-foot couplings were investigated. The curves among the lower limbs of the participants were compared using a statistical parametric mapping test. Compensatory strategies were found in the lower limbs from coordination patterns. In thigh-shank coupling, although distinct coordination traits in stance and swing phases among the lower limbs were found, the lower limbs in both groups were discovered to remain in a similar coordination pattern during gait. For individuals with uTFA, in shank-foot coupling, intact limbs demonstrated a short period of foot-leading pattern which was significantly different from that of the other limbs during mid-stance to compensate for the weaker force generation by prosthetic limbs. The findings offer normative coordination patterns on the walking of individuals with uTFA, which could benefit prosthetic gait rehabilitation and development.

Longitudinal deep network for consistent OCT layer segmentation.

Retinal layer thickness is an important bio-marker for people with multiple sclerosis (PwMS). In clinical practice, retinal layer thickness changes in optical coherence tomography (OCT) are widely used for monitoring multiple sclerosis (MS) progression. Recent developments in automated retinal layer segmentation algorithms allow cohort-level retina thinning to be observed in a large study of PwMS. However, variability in these results make it difficult to identify patient-level trends; this prevents patient specific disease monitoring and treatment planning using OCT. Deep learning based retinal layer segmentation algorithms have achieved state-of-the-art accuracy, but the segmentation is performed on each individual scan without utilizing longitudinal information, which can be important in reducing segmentation error and reveal subtle changes in retinal layers. In this paper, we propose a longitudinal OCT segmentation network which achieves more accurate and consistent layer thickness measurements for PwMS.

Change of geographic distributions of the genus Morchella species responding to climate oscillations in East Asia.

Morchella is a rare macrofungi taxon with high medicinal and edible values. Influenced by recent climate oscillations and human activities, habitat fragmentation of this genus has been critical, leading to a rapid decline of the resource of Morchella. It is thus urgent to preserve Morchella species. Based on maximum entropy model (MaxEnt), and 102 geographic distribution records of Morchella species with 10 environmental factors, we simulated the changes of potential geographic distributions under the climatic conditions of the last glacial maximum (LGM), last interglacial (LIG), in contemporary period and future (2050, 2070). We further analyzed the potential changes of geographic distributions of Morchella species in East Asia under climate change and formulated the effective conservation strategies for Morchella. The results showed that the dominant environmental factors affecting the geographic distributions of Morchella species were mean temperature of coldest quarter, annual precipitation, elevation and temperature annual range, with the mean temperature of coldest quarter having the greatest contribution. Results of the species distribution models showed that the highly suitable regions for Morchella species were mainly distributed in parts of western China under contemporary period. From the LIG to LGM and then the current to the future period, the total suitable regions of Morchella species showed a trend of firstly decrease and then increase, while the highly suitable regions showed similar change with the total suitable regions. At present, there is an urgent need to conduct in situ conservation for the resources of Morchella species in highly suitable regions in western China, and to carry out ex situ conservation in the marginal ranges of highly suitable regions and moderately suitable regions of Shaanxi, Hebei, Shandong, and other regions in China.

TransMorph: Transformer for unsupervised medical image registration.

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently, Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.

Disentangled Representation Learning for OCTA Vessel Segmentation With Limited Training Data.

Optical coherence tomography angiography (OCTA) is an imaging modality that can be used for analyzing retinal vasculature. Quantitative assessment of en face OCTA images requires accurate segmentation of the capillaries. Using deep learning approaches for this task faces two major challenges. First, acquiring sufficient manual delineations for training can take hundreds of hours. Second, OCTA images suffer from numerous contrast-related artifacts that are currently inherent to the modality and vary dramatically across scanners. We propose to solve both problems by learning a disentanglement of an anatomy component and a local contrast component from paired OCTA scans. With the contrast removed from the anatomy component, a deep learning model that takes the anatomy component as input can learn to segment vessels with a limited portion of the training images being manually labeled. Our method demonstrates state-of-the-art performance for OCTA vessel segmentation.

Probing functional conformation-state fluctuation dynamics in recognition binding between calmodulin and target peptide.

Conformational dynamics play a crucial role in protein functions. A molecular-level understanding of the conformational transition dynamics of proteins is fundamental for studying protein functions. Here, we report a study of real-time conformational dynamic interaction between calcium-activated calmodulin (CaM) and C28W peptide using single-molecule fluorescence resonance energy transfer (FRET) spectroscopy and imaging. Plasma membrane Ca-ATPase protein interacts with CaM by its peptide segment that contains 28 amino acids (C28W). The interaction between CaM and the Ca-ATPase is essential for cell signaling. However, details about its dynamic interaction are still not clear. In our current study, we used Cyanine3 labeled CaM (N-domain) and Dylight 649 labeled C28W peptide (N-domain) to study the conformational dynamics during their interaction. In this study, the FRET can be measured when the CaM-C28W complex is formed and only be observed when such a complex is formed. By using single-molecule FRET efficiency trajectory and unique statistical approaches, we were able to observe multiple binding steps with detailed dynamic features of loosely bound and tightly bound state fluctuations. The C-domain of CaM tends to bind with C28W first with a higher affinity, followed by the binding of the CaM N-domain. Due to the comparatively high flexibility and low affinity of the N-domain and the presence of multiple anchor hydrophobic residues on the peptide, the N-domain binding may switch between selective and non-selective binding states, while the C-domain remains strongly bound with C28W. The results provide a mechanistic understanding of the CaM signaling interaction and activation of the Ca-ATPase through multiple-state binding to the C28W. The new single-molecule spectroscopic analyses demonstrated in this work can be applied for broad studies of protein functional conformation fluctuation and protein-protein interaction dynamics.

Optic Neuritis-Independent Retinal Atrophy in Neuromyelitis Optica Spectrum Disorder.

BACKGROUND: A limited number of studies have investigated the presence of ongoing disease activity independent of clinical relapses in neuromyelitis optica spectrum disorder (NMOSD), and data are conflicting. The objective of our study was to examine whether patients with aquaporin-4 (AQP4)-IgG seropositive NMOSD exhibit progressive retinal neuroaxonal loss, independently of optic neuritis (ON) attacks. METHODS: In this single-center, longitudinal study, 32 AQP4-IgG+ NMOSD patients and 48 healthy controls (HC) were followed with serial spectral-domain optical coherence tomography and visual acuity (VA) assessments. NMOSD patients with ON less than 6 months before baseline were excluded, whereas data from patients with ON during follow-up were censored at the last visit before ON. VA worsening was defined as a decrease in monocular letter acuity ≥5 letters for high-contrast VA and ≥7 letters for low-contrast VA. Analyses were performed with mixed-effects linear regression models adjusted for age, sex, and race. RESULTS: The median follow-up duration was 4.2 years (interquartile range: 1.8-7.5). Relative to HC, NMOSD eyes had faster peripapillary retinal nerve fiber layer (pRNFL) (ß = -0.25 µm/year faster, 95% confidence interval [CI]: -0.45 to -0.05, P = 0.014) and GCIPL thinning (ß = -0.09 µm/year faster, 95% CI: -0.17 to 0, P = 0.05). This difference seemed to be driven by faster pRNFL and GCIPL thinning in NMOSD eyes without a history of ON compared with HC (GCIPL: ß = -0.15 µm/year faster; P = 0.005; pRNFL: ß = -0.43 µm/year faster, P < 0.001), whereas rates of pRNFL (ß: -0.07 µm/year, P = 0.53) and GCIPL (ß = -0.01 µm/year, P = 0.90) thinning did not differ between NMOSD-ON and HC eyes. Nine NMOSD eyes had VA worsening during follow-up. CONCLUSIONS: In this longitudinal study, we observed progressive pRNFL and GCIPL atrophy in AQP4-IgG+ NMOSD eyes unaffected by ON. These results support that subclinical involvement of the anterior visual pathway may occur in AQP4-IgG+ NMOSD.

Conformational states and fluctuations in endothelial nitric oxide synthase under calmodulin regulation.

Mechanisms that regulate nitric oxide synthase enzymes (NOS) are of interest in biology and medicine. Although NOS catalysis relies on domain motions and is activated by calmodulin (CaM) binding, the relationships are unclear. We used single-molecule fluorescence resonance energy transfer (FRET) spectroscopy to elucidate the conformational states distribution and associated conformational fluctuation dynamics of the two NOS electron transfer domains in an FRET dye-labeled endothelial NOS reductase domain (eNOSr) and to understand how CaM affects the dynamics to regulate catalysis by shaping the spatial and temporal conformational behaviors of eNOSr. In addition, we developed and applied a new imaging approach capable of recording three-dimensional FRET efficiency versus time images to characterize the impact on dynamic conformal states of the eNOSr enzyme by the binding of CaM, which identifies clearly that CaM binding generates an extra new open state of eNOSr, resolving more detailed NOS conformational states and their fluctuation dynamics. We identified a new output state that has an extra open conformation that is only populated in the CaM-bound eNOSr. This may reveal the critical role of CaM in triggering NOS activity as it gives conformational flexibility for eNOSr to assume the electron transfer output FMN-heme state. Our results provide a dynamic link to recently reported EM static structure analyses and demonstrate a capable approach in probing and simultaneously analyzing all of the conformational states, their fluctuations, and the fluctuation dynamics for understanding the mechanism of NOS electron transfer, involving electron transfer among FAD, FMN, and heme domains, during nitric oxide synthesis.

Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory.

In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches.

Autoencoder based self-supervised test-time adaptation for medical image analysis.

Deep neural networks have been successfully applied to medical image analysis tasks like segmentation and synthesis. However, even if a network is trained on a large dataset from the source domain, its performance on unseen test domains is not guaranteed. The performance drop on data obtained differently from the network's training data is a major problem (known as domain shift) in deploying deep learning in clinical practice. Existing work focuses on retraining the model with data from the test domain, or harmonizing the test domain's data to the network training data. A common practice is to distribute a carefully-trained model to multiple users (e.g., clinical centers), and then each user uses the model to process their own data, which may have a domain shift (e.g., varying imaging parameters and machines). However, the lack of availability of the source training data and the cost of training a new model often prevents the use of known methods to solve user-specific domain shifts. Here, we ask whether we can design a model that, once distributed to users, can quickly adapt itself to each new site without expensive retraining or access to the source training data? In this paper, we propose a model that can adapt based on a single test subject during inference. The model consists of three parts, which are all neural networks: a task model (T) which performs the image analysis task like segmentation; a set of autoencoders (AEs); and a set of adaptors (As). The task model and autoencoders are trained on the source dataset and can be computationally expensive. In the deployment stage, the adaptors are trained to transform the test image and its features to minimize the domain shift as measured by the autoencoders' reconstruction loss. Only the adaptors are optimized during the testing stage with a single test subject thus is computationally efficient. The method was validated on both retinal optical coherence tomography (OCT) image segmentation and magnetic resonance imaging (MRI) T1-weighted to T2-weighted image synthesis. Our method, with its short optimization time for the adaptors (10 iterations on a single test subject) and its additional required disk space for the autoencoders (around 15 MB), can achieve significant performance improvement. Our code is publicly available at: https://github.com/YufanHe/self-domain-adapted-network.

Structured layer surface segmentation for retina OCT using fully convolutional regression networks.

Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer resolution which has been widely used for scanning the retina. Retinal layers are important biomarkers for many diseases. Accurate automated algorithms for segmenting smooth continuous layer surfaces with correct hierarchy (topology) are important for automated retinal thickness and surface shape analysis. State-of-the-art methods typically use a two step process. Firstly, a trained classifier is used to label each pixel into either background and layers or boundaries and non-boundaries. Secondly, the desired smooth surfaces with the correct topology are extracted by graph methods (e.g., graph cut). Data driven methods like deep networks have shown great ability for the pixel classification step, but to date have not been able to extract structured smooth continuous surfaces with topological constraints in the second step. In this paper, we combine these two steps into a unified deep learning framework by directly modeling the distribution of the surface positions. Smooth, continuous, and topologically correct surfaces are obtained in a single feed forward operation. The proposed method was evaluated on two publicly available data sets of healthy controls and subjects with either multiple sclerosis or diabetic macular edema, and is shown to achieve state-of-the art performance with sub-pixel accuracy.

Evidence of subclinical quantitative retinal layer abnormalities in AQP4-IgG seropositive NMOSD.

BACKGROUND: Prior studies have suggested that subclinical retinal abnormalities may be present in aquaporin-4 immunoglobulin G (AQP4-IgG) seropositive neuromyelitis optica spectrum disorder (NMOSD), in the absence of a clinical history of optic neuritis (ON). OBJECTIVE: Our aim was to compare retinal layer thicknesses at the fovea and surrounding macula between AQP4-IgG+ NMOSD eyes without a history of ON (AQP4-nonON) and healthy controls (HC). METHODS: In this single-center cross-sectional study, 83 AQP4-nonON and 154 HC eyes were studied with spectral-domain optical coherence tomography (OCT). RESULTS: Total foveal thickness did not differ between AQP4-nonON and HC eyes. AQP4-nonON eyes exhibited lower outer nuclear layer (ONL) and inner photoreceptor segment (IS) thickness at the fovea (ONL: -4.01 ± 2.03 µm, p = 0.049; IS: -0.32 ± 0.14 µm, p = 0.029) and surrounding macula (ONL: -1.98 ± 0.95 µm, p = 0.037; IS: -0.16 ± 0.07 µm, p = 0.023), compared to HC. Macular retinal nerve fiber layer (RNFL: -1.34 ± 0.51 µm, p = 0.009) and ganglion cell + inner plexiform layer (GCIPL: -2.44 ± 0.93 µm, p = 0.009) thicknesses were also lower in AQP4-nonON compared to HC eyes. Results were similar in sensitivity analyses restricted to AQP4-IgG+ patients who had never experienced ON in either eye. CONCLUSIONS: AQP4-nonON eyes exhibit evidence of subclinical retinal ganglion cell neuronal and axonal loss, as well as structural evidence of photoreceptor layer involvement. These findings support that subclinical anterior visual pathway involvement may occur in AQP4-IgG+ NMOSD.

Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization.

The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a way to study regional cerebellar atrophy and also provides an anatomical map for functional imaging. In a recent comparison, a multi-atlas approach proved to be superior to other parcellation methods including some based on convolutional neural networks (CNNs) which have a considerable speed advantage. In this work, we developed an alternative CNN design for cerebellar parcellation, yielding a method that achieves the leading performance to date. The proposed method was evaluated on multiple data sets to show its broad applicability, and a Singularity container has been made publicly available.

Fully Convolutional Boundary Regression for Retina OCT Segmentation.

A major goal of analyzing retinal optical coherence tomography (OCT) images is retinal layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer surfaces, with correct hierarchy (topology) are desired for monitoring disease progression. State-of-the-art methods use a trained classifier to label each pixel into background, layer, or surface pixels. The final step of extracting the desired smooth surfaces with correct topology are mostly performed by graph methods (e.g. shortest path, graph cut). However, manually building a graph with varying constraints by retinal region and pathology and solving the minimization with specialized algorithms will degrade the flexibility and time efficiency of the whole framework. In this paper, we directly model the distribution of surface positions using a deep network with a fully differentiable soft argmax to obtain smooth, continuous surfaces in a single feed forward operation. A special topology module is used in the deep network both in the training and testing stages to guarantee the surface topology. An extra deep network output branch is also used for predicting lesion and layers in a pixel-wise labeling scheme. The proposed method was evaluated on two publicly available data sets of healthy controls, subjects with multiple sclerosis, and diabetic macular edema; it achieves state-of-the art sub-pixel results.

Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT.

Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.

Layer boundary evolution method for macular OCT layer segmentation.

Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra- and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.

Parallel deep neural networks for endoscopic OCT image segmentation.

We report parallel-trained deep neural networks for automated endoscopic OCT image segmentation feasible even with a limited training data set. These U-Net-based deep neural networks were trained using a modified dice loss function and manual segmentations of ultrahigh-resolution cross-sectional images collected by an 800 nm OCT endoscopic system. The method was tested on in vivo guinea pig esophagus images. Results showed its robust layer segmentation capability with a boundary error of 1.4 µm insensitive to lay topology disorders. To further illustrate its clinical potential, the method was applied to differentiating in vivo OCT esophagus images from an eosinophilic esophagitis (EOE) model and its control group, and the results clearly demonstrated quantitative changes in the top esophageal layers' thickness in the EOE model.

Retinal layer parcellation of optical coherence tomography images: Data resource for multiple sclerosis and healthy controls.

This paper presents optical coherence tomography (OCT) images of the human retina and manual delineations of eight retinal layers. The data includes 35 human retina scans acquired on a Spectralis OCT system (Heidelberg Engineering, Heidelberg, Germany), 14 of which are healthy controls (HC) and 21 have a diagnosis of multiple sclerosis (MS). The provided data includes manually delineation of eight retina layers, which were independently reviewed and edited. The data presented in this article was used to validate automatic segmentation algorithms (Lang et al., 2013).