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Balance control, a complex sensorimotor skill, declines with age. Assessing balance is crucial for identifying fall risk and implementing interventions in the older population. This study aimed to measure age-dependent changes in static and dynamic balance using inertial measurement units in a clinical setting. This study included 82 healthy participants aged 20–85 years. For the dynamic balance test, participants stood on a horizontally swaying balance board. For the static balance test, they stood on one leg. Inertial measurement units attached to their bodies recorded kinematic data, with average absolute angular velocities assessing balance capabilities. In the dynamic test, the younger participants had smaller average absolute angular velocities in most body parts than those of the middle-aged and older groups, with no significant differences between the middle-aged and older groups. Conversely, in the single-leg stance tests, the young and middle-aged groups outperformed the older group, with no significant differences between the young and middle-aged groups. Thus, dynamic and static balance decline at different stages with age. These results highlight the complementary role of inertial measurement unit-based evaluation in understanding the effect of age on postural control mechanisms, offering valuable insights for tailoring rehabilitation protocols in clinical settings.

Background Missing values in datasets present significant challenges for data analysis, particularly in the medical field where data accuracy is crucial for patient diagnosis and treatment. Although MissForest (MF) has demonstrated efficacy in imputation research and recursive feature elimination (RFE) has proven effective in feature selection, the potential for enhancing MF through RFE integration remains unexplored. Methods This study introduces a novel imputation method, “recursive feature elimination-MissForest” (RFE-MF), designed to enhance imputation quality by reducing the impact of irrelevant features. A comparative analysis is conducted between RFE-MF and four classical imputation methods: mean/mode, k-nearest neighbors (kNN), multiple imputation by chained equations (MICE), and MF. The comparison is carried out across ten medical datasets containing both numerical and mixed data types. Different missing data rates, ranging from 10 to 50%, are evaluated under the missing completely at random (MCAR) mechanism. The performance of each method is assessed using two evaluation metrics: normalized root mean squared error (NRMSE) and predictive fidelity criterion (PFC). Additionally, paired samples t -tests are employed to analyze the statistical significance of differences among the outcomes. Results The findings indicate that RFE-MF demonstrates superior performance across the majority of datasets when compared to four classical imputation methods (mean/mode, kNN, MICE, and MF). Notably, RFE-MF consistently outperforms the original MF, irrespective of variable type (numerical or categorical). Mean/mode imputation exhibits consistent performance across various scenarios. Conversely, the efficacy of kNN imputation fluctuates in relation to varying missing data rates. Conclusion This study demonstrates that RFE-MF holds promise as an effective imputation method for medical datasets, providing a novel approach to addressing missing data challenges in medical applications.

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM .

Knee osteoarthritis (OA) is a prevalent condition in older adults that often results in impaired gait and balance, increased risk of falls, and reduced quality of life. Conventional clinical assessments may not adequately capture these deficiencies. This study investigated the gait and balance of elderly individuals with knee OA using wearable inertial measurement units (IMUs). Forty-four participants with Kellgren–Lawrence grade 2–3 knee OA (71.23 ± 5.75 years) and forty-five age-matched controls (70.87 ± 4.30 years) completed dynamic balance (balance board), static balance (single-leg stance), ‘timed up and go’ (TUG), and normal walking tasks. Between 2 and 8 IMUs, depending on the task, were placed on the head, chest, waist, knees, ankles, soles, and balance board to record kinematic data. Balance was quantified using absolute angular velocity and linear acceleration, with group differences analyzed by MANOVA and Bonferroni-adjusted univariate tests. The participants with knee OA exhibited greater gait asymmetry, although the difference was not significant. However, they consistently demonstrated higher absolute angular velocities than controls across most body segments during static and dynamic tasks, indicating reduced postural stability. No group differences were observed in TUG performance. These findings suggest that IMU-based measures, particularly angular velocity, are sensitive to balance impairment detection in knee OA. Incorporating IMU technology into clinical assessments may facilitate early identification of instability and guide targeted interventions to reduce fall risk.

We propose a speed-adapted treadmill that can be incorporated into a rehabilitation trainer that applies neurodevelopmental treatment (NDT) for patients with stroke. NDT practice is effective for post-stroke patients, but its requirement for therapists’ participation can limit the patients’ rehabilitation during the golden period of recovery. Previous studies have proposed a trainer that can automatically reiterate therapists’ interventions. However, that trainer employed a constant-speed treadmill, which required the users to frequently adjust their walking speeds during rehabilitation. This paper develops a speed-adapted treadmill that can regulate the treadmill motor to maintain the subject’s position during the training process. First, we derive models of the treadmill and cable motors through experiments. Then, we design robust controls for the two systems and simplify them as proportional-integral-derivative controllers for hardware implementation. Finally, we integrate the system and invite healthy and stroke subjects to participate in clinical experiments. Among ten stroke subjects, all subjects’ walking speeds and nine subjects’ stride lengths were improved, while eight subjects showed improvement in the swing-phase asymmetry and pelvic rotation after receiving the NDT rehabilitation employing the speed-adapted treadmill. Our findings indicate that the NDT trainer effectively enhances users’ gait characteristics, including swing-phase symmetry, pelvic rotation, walking speed, and stride length.

Background This work aims to help develop new protein engineering techniques based on a structural rearrangement phenomenon called circular permutation (CP), equivalent to connecting the native termini of a protein followed by creating new termini at another site. Although CP has been applied in many fields, its implementation is still costly because of inevitable trials and errors. Results Here we present CirPred, a structure modeling and termini linker design method for circularly permuted proteins. Compared with state-of-the-art protein structure modeling methods, CirPred is the only one fully capable of both circularly-permuted modeling and traditional co-linear modeling. CirPred performs well when the permutant shares low sequence identity with the native protein and even when the permutant adopts a different conformation from the native protein because of three-dimensional (3D) domain swapping. Linker redesign experiments demonstrated that the linker design algorithm of CirPred achieved subangstrom accuracy. Conclusions The CirPred system is capable of (1) predicting the structure of circular permutants, (2) designing termini linkers, (3) performing traditional co-linear protein structure modeling, and (4) identifying the CP-induced occurrence of 3D domain swapping. This method is supposed helpful for broadening the application of CP, and its web server is available at http://10.life.nctu.edu.tw/CirPred/ and http://lo.life.nctu.edu.tw/CirPred/ .

Background Automatic segmentation of hepatocellular carcinoma (HCC) on computed tomography (CT) scans is in urgent need to assist diagnosis and radiomics analysis. The aim of this study is to develop a deep learning based network to detect HCC from dynamic CT images. Methods Dynamic CT images of 595 patients with HCC were used. Tumors in dynamic CT images were labeled by radiologists. Patients were randomly divided into training, validation and test sets in a ratio of 5:2:3, respectively. We developed a hierarchical fusion strategy of deep learning networks (HFS-Net). Global dice, sensitivity, precision and F1-score were used to measure performance of the HFS-Net model. Results The 2D DenseU-Net using dynamic CT images was more effective for segmenting small tumors, whereas the 2D U-Net using portal venous phase images was more effective for segmenting large tumors. The HFS-Net model performed better, compared with the single-strategy deep learning models in segmenting small and large tumors. In the test set, the HFS-Net model achieved good performance in identifying HCC on dynamic CT images with global dice of 82.8%. The overall sensitivity, precision and F1-score were 84.3%, 75.5% and 79.6% per slice, respectively, and 92.2%, 93.2% and 92.7% per patient, respectively. The sensitivity in tumors < 2 cm, 2–3, 3–5 cm and > 5 cm were 72.7%, 92.9%, 94.2% and 100% per patient, respectively. Conclusions The HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis.

This study presents the design and optimization of a digital-imaging afocal telescope system that integrates an afocal telescope architecture with an imaging optical subsystem. The proposed system employs a combination of spherical and aspherical optical elements to enhance imaging flexibility, reduce aberrations, and ensure effective system coupling. Proper pupil matching is achieved by aligning the exit pupil of the afocal telescope with the entrance pupil of the imaging system, ensuring minimal vignetting and optimal energy transfer. Circular apertures and lens elements are used throughout the system to simplify alignment and minimize pupil-matching errors. The complete system comprises three imaging optical subsystems and a digital camera module, each independently optimized to ensure balanced optical performance. The design achieves an overall magnification of 16×, with near-diffraction-limited quality confirmed by an RMS wavefront error of 0.0474λ and a Strehl ratio of 0.915. The modulation transfer function (MTF) reaches 0.42 at 80 lp/mm, while the distortion remains below 4.87%. Chromatic performance is well controlled, with maximum lateral color deviations of 1.007 µm (short-to-long wavelength) and 1.52 µm (short-to-reference wavelength), evaluated at 656 nm, 587 nm, and 486 nm. The results demonstrate that the proposed digital-imaging afocal telescope system provides high-resolution, low-aberration imaging suitable for precision optical applications.

This study reveals the mechanisms behind the ultralow lattice thermal conductivity κL in β‐Zn4Sb3 single crystals through inelastic neutron scattering (INS). Analyzing phonon behaviors and the interaction between acoustic phonons and rattling modes, the first experimental evidence of avoided crossing in β‐Zn4Sb3 is provided. The rattler‐phonon avoided crossings contribute to the low κL in a β‐Zn4Sb3 single crystal, enhancing the thermoelectric figure‐of‐merit (zT). TEM characterizations of the β‐Zn4Sb3 single crystal with intrinsic and ultralow κL reveal a grain‐boundary‐free structure with uniformly dispersed rotation moiré fringes that contribute to low lattice thermal conductivity while maintaining a uniform elemental distribution. Additionally, the significant impact of crystallinity control coupled with dilute doping on boosting thermoelectric performance, with single‐crystalline single leg outperforming their polycrystalline counterparts is demonstrated. Notably, the conversion efficiency η of the undoped β‐Zn4Sb3 single leg achieves 1.4% under a temperature gradient of 200 K. This research uncovers the origins of ultralow lattice thermal conductivity in β‐Zn₄Sb₃, presenting the first experimental proof of avoided crossings between acoustic phonons and rattling modes via inelastic neutron scattering while demonstrating how crystallinity control significantly enhances thermoelectric performance, leading to a promising conversion efficiency of 1.6% in single‐crystalline β‐Zn₄Sb₃ single‐leg under a 175 K temperature gradient.

PurposeChronic rejection induces the occurrence of orthotopic allograft transplantation (OAT) vasculopathy, which results in failure of the donor organ. Numerous studies have demonstrated that in addition to regulating blood sugar homeostasis, dipeptidyl peptidase-4 (DPP-4) inhibitors can also provide efficacious therapeutic and protective effects against cardiovascular diseases. However, their effects on OAT-induced vasculopathy remain unknown. Thus, the aim of this study was to investigate the direct effects of sitagliptin on OAT vasculopathy in vivo and in vitro.MethodsThe PVG/Seac rat thoracic aorta graft to ACI/NKyo rat abdominal aorta model was used to explore the effects of sitagliptin on vasculopathy. Human endothelial progenitor cells (EPCs) were used to investigate the possible underlying mechanisms.ResultsWe demonstrated that sitagliptin decreases vasculopathy in OAT ACI/NKyo rats. Treatment with sitagliptin decreased BNP and HMGB1 levels, increased GLP-1 activity and stromal cell-derived factor 1α (SDF-1α) expression, elevated the number of circulating EPCs, and improved the differentiation possibility of mononuclear cells to EPCs ex vivo. However, in vitro studies showed that recombinant B-type natriuretic peptide (BNP) and high mobility group box 1 (HMGB1) impaired EPC function, whereas these phenomena were reversed by glucagon-like peptide 1 (GLP-1) receptor agonist treatment.ConclusionsWe suggest that the mechanisms underlying sitagliptin-mediated inhibition of OAT vasculopathy probably occur through a direct increase in GLP-1 activity. In addition to the GLP-1-dependent pathway, sitagliptin may regulate SDF-1α levels and EPC function to reduce OAT-induced vascular injury. This study may provide new prevention and treatment strategies for DPP-4 inhibitors in chronic rejection-induced vasculopathy.