Dynamic reconfiguration of brain coactivation states that underlying working memory correlates with cognitive decline in clinically unimpaired older adults.

Impairments in working memory (WM) are evident in both clinically diagnosed patients with mild cognitive decline and older adults at risk, as indicated by lower scores on neuropsychological tests. Examining the WM-related neural signatures in at-risk older adults becomes essential for timely intervention. WM functioning relies on dynamic brain activities, particularly within the frontoparietal system. However, it remains unclear whether the cognitive decline would be reflected in the decreased dynamic reconfiguration of brain coactivation states during WM tasks. We enrolled 47 older adults and assessed their cognitive function using the Montreal Cognitive Assessment. The temporal dynamics of brain coactivations during a WM task were investigated through graph-based time-frame modularity analysis. Four primary recurring states emerged: two task-positive states with positive activity in the frontoparietal system (dorsal attention and central executive); two task-negative states with positive activity in the default mode network accompanied by negative activity in the frontoparietal networks. Heightened WM load was associated with increased flexibility of the frontoparietal networks, but the cognitive decline was correlated with reduced capacity for neuroplastic changes in response to increased task demands. These findings advance our understanding of aberrant brain reconfiguration linked to cognitive decline, potentially aiding early identification of at-risk individuals.

Polyploid cardiomyocytes: implications for heart regeneration.

Terminally differentiated cells are generally thought to have arrived at their final form and function. Many terminally differentiated cell types are polyploid, i.e. they have multiple copies of the normally diploid genome. Mammalian heart muscle cells, termed cardiomyocytes, are one such example of polyploid cells. Terminally differentiated cardiomyocytes are bi- or multi-nucleated, or have polyploid nuclei. Recent mechanistic studies of polyploid cardiomyocytes indicate that they can limit cellular proliferation and, hence, heart regeneration. In this short Spotlight, we present the mechanisms generating bi- and multi-nucleated cardiomyocytes, and the mechanisms generating polyploid nuclei. Our aim is to develop hypotheses about how these mechanisms might relate to cardiomyocyte proliferation and cardiac regeneration. We also discuss how these new findings could be applied to advance cardiac regeneration research, and how they relate to studies of other polyploid cells, such as cancer cells.

N6-methyladenosine modification of PLOD2 causes spermatocyte damage in rats with varicocele.

BACKGROUND: In recent years, N6-methyladenosine (m6A) methylation modification of mRNA has been studied extensively. It has been reported that m6A determines mRNA fate and participates in many cellular functions and reactions, including oxidative stress. The PLOD2 gene encodes a protein that plays a key role in tissue remodeling and fibrotic processes. METHODS: The m6A methylation and expression levels of PLOD2 were determined by m6A methylated RNA immunoprecipitation sequencing (MeRIP-seq) and MeRIP-quantitative polymerase chain reaction (qPCR) in the testes of varicocele rats compared with control. To determine whether IGF2BP2 had a targeted effect on the PLOD2 mRNA, RNA immunoprecipitation-qPCR (RIP-qPCR) and luciferase assays were performed. CRISPR/dCas13b-ALKBH5 could downregulate m6A methylation level of PLOD2, which plays an important role in PLOD2-mediated cell proliferation and apoptosis in GC-2 cells. RESULTS: PLOD2 was frequently exhibited with high m6A methylation and expression level in the testes of varicocele rats compared with control. In addition, we found that IGF2BP2 binds to the m6A-modified 3' untranslated region (3'-UTR) of PLOD2 mRNA, thereby positively regulating its mRNA stability. Targeted specific demethylation of PLOD2 m6A by CRISPR/dCas13b-ALKBH5 system can significantly decrease the m6A and expression level of PLOD2. Furthermore, demethylation of PLOD2 mRNA dramatically promote GC-2 cell proliferation and inhibit cell apoptosis under oxidative stress. CONCLUSION: As a result, we found that varicocele-induced oxidative stress promoted PLOD2 expression level via m6A methylation modification. In addition, targeting m6A demethylation of PLOD2 by CRISPR/dCas13b-ALKBH5 system can regulate GC-2 cell proliferation and apoptosis under oxidative stress. Taken together, our study has acquired a better understanding of the mechanisms underlying male infertility associated with oxidative stress, as well as a novel therapeutic target for male infertility.

Synthesis of Well-Ordered Mesoporous Aluminosilicates with High Aluminum Contents: The Challenge and the Promise.

SBA-15 has recently emerged as a potential material for the catalytic conversion of large molecules. Usually, SBA-15 has a low content of aluminum due to the conventional acidic synthesis medium. Although a few approaches have been adopted to prepare Al-SBA-15 with a high alumina content, it is still challenging to prepare well-ordered Al-SBA-15 with a high alumina content. Here, we demonstrate a facile synthesis process in neutral mediums for the grafting of Al into the framework of SBA-15. This approach relies mainly on the dissociation of Si-O-Si bonds and the polymerization of Si-O-Si/Al bonds promoted by sodium persulfate (SPS) in neutral mediums. In this way, well-ordered Al-SBA-15 with a high aluminum content and enhanced acidity was obtained. Results of X-ray fluorescence spectroscopy (XRF) showed an n(SiO2)/n(Al2O3) ratio of 13.7, much lower than that of the conventional sample (21.7) obtained in acidic medium. The characterization results indicated the presence of a well-ordered Al-containing mesophase with high hydrothermal stability. Notably, the Al content and the acidity of Al-SBA-15 can be tuned by changing the SPS amount.

Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.

Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors within the prosthesis to determine which ambulation mode the user is in. However, it can be challenging to distinguish between ambulation modes. Efforts have been made to improve classification accuracy by adding electromyography information, but this requires a large number of sensors, has a low signal-to-noise ratio, and cannot distinguish between superficial and deep muscle activations. An alternative sensing modality, A-mode ultrasound, can detect and distinguish between changes in superficial and deep muscles. It has also shown promising results in upper limb gesture classification. Despite these advantages, A-mode ultrasound has yet to be employed for lower limb activity classification. Here we show that A- mode ultrasound can classify ambulation mode with comparable, and in some cases, superior accuracy to mechanical sensing. In this study, seven transfemoral amputee subjects walked on an ambulation circuit while wearing A-mode ultrasound transducers, IMU sensors, and their passive prosthesis. The circuit consisted of sitting, standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent, and a spatial-temporal convolutional network was trained to continuously classify these seven activities. Offline continuous classification with A-mode ultrasound alone was able to achieve an accuracy of 91.8±3.4%, compared with 93.8±3.0%, when using kinematic data alone. Combined kinematic and ultrasound produced 95.8±2.3% accuracy. This suggests that A-mode ultrasound provides additional useful information about the user's gait beyond what is provided by mechanical sensors, and that it may be able to improve ambulation mode classification. By incorporating these sensors into powered prostheses, users may enjoy higher reliability for their prostheses, and more seamless transitions between ambulation modes.

Obtaining of Mesoporous Aluminosilicates with High Hydrothermal Stability by Composite Organic Templates: Utility and Mechanism.

We have reported the synthesis of mesoporous aluminosilicates (MAs) with high hydrothermal stability via assembly of basic characteristic structure units of typical microporous zeolite Y. In spite of this, high consumption of organic template and H2O remains a major obstacle to its industrial application. Herein, a facile and effective strategy called "composite templates" was employed to decrease significantly the amount of P123 and H2O. In this method, composite micelles of P123/poly(vinyl alcohol) (PVA) could be more easily dispersed in the solution due to the lowering of water's surface tension caused by the free hydroxyl groups of PVA. Moreover, the improved assembly ability of composite micelles in high concentration solution leads to the synthesis of hydrothermally stable MAs with 45% decrease of organic template P123 and 81% that of water amount. It was found that by the introduction of composite templates, the textural properties such as the surface area of materials, volume of pore, size of pore, and thickness were enlarged simultaneously. Meanwhile, this article presented an understanding into the assembly of composite micelles in the process of synthesis of MAs.

Fiber Bragg Grating Displacement Sensor with High Abrasion Resistance for a Steel Spring Floating Slab Damping Track.

This paper presents a fiber Bragg grating (FBG) displacement sensor with high abrasion resistance for displacement monitoring of a steel spring floating slab damping track. A wedge-shaped sliding block and an equal-strength beam form a conversion mechanism to transfer displacement to the deflection of the beam, and the deflection-induced strain is exerted on two FBGs. A special linear guide rail-slider and a precision rolling bearing have been adopted onto the conversion mechanism, which turned sliding friction into rolling friction and thus significantly reduced the friction during frequent alternating displacement measuring. Sensing principle and the corresponding theoretical derivation have been demonstrated. Experiment results show that the sensor has a sensitivity of 34.32 pm/mm and a high resolution of 0.0029 mm within a measurement range of 0~90 mm. Besides, the sensor has also a good measurement capability for micro-displacement within a range of 0~3 mm. The repeatability error and hysteresis error are 1.416% and 0.323%, respectively. Good creep resistance and high abrasion resistance for alternating displacement measurement have also been presented by a performance test. These excellent performances satisfy the requirements of high precision and long-term stability in structural health monitoring for machinery equipment and civil engineering, especially in the displacement monitoring of a floating slab damping track.

Extraction of biomedical compounds from the wood of Pterocarpus macarocarpus Kurz heartwood.

Some wood can be used as traditional Chinese medicine. The medicinal value of wood is associated with its extractives. Pterocarpus macarocarpus Kurz heartwood is a kind of top valuable reddish hardwood in making furniture and handicrafts, but the research about medicine value of this wood is not enough. In order to investigate the high value biomedical compounds in Pterocarpus macarocarpus Kurz heartwood, the woody extractives were obtained by Soxhlet extraction and ultrasonic extraction with benzene-ethanol (1:2, v/v) solvent simultaneously and were analyzed by Gas Chromatography-Mass Spectrometer (GC-MS). Combining with the results of the two extraction methods, 44 compounds can be identified in total. Amony these identified compounds, there were 5 flavonoids, 15 terpenes and 3 steroidal compounds. The representative biomedical compositions were homopterocarpin, medicarpin, (-)-pterocarpin, formononetin, ß-eudesmol, stigmasterol, linoleic acid and so on, which indicated that the extractives from Pterocarpus macarocarpus Kurz heartwood have huge potential in biomedicine. This research provides scientific basis for further comprehensive utilization of Pterocarpus macarocarpus Kurz heartwood as Chinese medicine.

An Interactive Image Segmentation Method in Hand Gesture Recognition.

In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy.

Simultaneous Calibration: A Joint Optimization Approach for Multiple Kinect and External Cameras.

Camera calibration is a crucial problem in many applications, such as 3D reconstruction, structure from motion, object tracking and face alignment. Numerous methods have been proposed to solve the above problem with good performance in the last few decades. However, few methods are targeted at joint calibration of multi-sensors (more than four devices), which normally is a practical issue in the real-time systems. In this paper, we propose a novel method and a corresponding workflow framework to simultaneously calibrate relative poses of a Kinect and three external cameras. By optimizing the final cost function and adding corresponding weights to the external cameras in different locations, an effective joint calibration of multiple devices is constructed. Furthermore, the method is tested in a practical platform, and experiment results show that the proposed joint calibration method can achieve a satisfactory performance in a project real-time system and its accuracy is higher than the manufacturer's calibration.

Interactive relationship between basement-membrane development and sarcomerogenesis in single cardiomyocytes.

The cardiac basement membrane (BM), the highly organized layer of the extracellular matrix (ECM) on the external side of the sarcolemma, is mainly composed of laminin and collagen IV, which assemble a dense, well-organized network to surround the surface of each adult cardiomyocyte. The development of the cardiac BM plays a key role in organogenesis of the myocardium through interactions between sarcomeres and integrins. Because of the complicated structure of cardiac muscle fibers and lack of a proper investigation method, the detailed interactions among BM development, sarcomeric growth, and integrin expression remain unclear. In this study, freshly isolated 3-day neonatal cardiomyocytes (CMs) were cultured on aligned collagen, which mimics the in vivo ECM structure and induces neonatal CMs to grow into rod-like shapes. Then double fluorescence-immunostained laminin and α-actinin or integrin ß1 on neonatal CMs cultured 4-72 h were imaged using a confocal microscope, and the spatial relationship between laminin deposition and α-actinin expression was evaluated by colocalization analysis. At 4h, laminin was deposited around Z-bodies (dot-shaped α-actinin) and integrins; from 18-to-72 h, its gradual colocalization with Z-lines (line-shaped α-actinin) and integrins increased Pearson׳s coefficient; this indicates that development of the BM network from the neonatal stage to adulthood is closely related to sarcomeric formation via integrin-mediated interactions.

Tracking Multiple Video Targets with an Improved GM-PHD Tracker.

Tracking multiple moving targets from a video plays an important role in many vision-based robotic applications. In this paper, we propose an improved Gaussian mixture probability hypothesis density (GM-PHD) tracker with weight penalization to effectively and accurately track multiple moving targets from a video. First, an entropy-based birth intensity estimation method is incorporated to eliminate the false positives caused by noisy video data. Then, a weight-penalized method with multi-feature fusion is proposed to accurately track the targets in close movement. For targets without occlusion, a weight matrix that contains all updated weights between the predicted target states and the measurements is constructed, and a simple, but effective method based on total weight and predicted target state is proposed to search the ambiguous weights in the weight matrix. The ambiguous weights are then penalized according to the fused target features that include spatial-colour appearance, histogram of oriented gradient and target area and further re-normalized to form a new weight matrix. With this new weight matrix, the tracker can correctly track the targets in close movement without occlusion. For targets with occlusion, a robust game-theoretical method is used. Finally, the experiments conducted on various video scenarios validate the effectiveness of the proposed penalization method and show the superior performance of our tracker over the state of the art.

Enzyme-etching technique to fabricate micropatterns of aligned collagen fibrils.

A technique to tailor-make pre-coated, pre-aligned bovine collagen fibrils, derived from neonatal cardiomyocytes, on the surface of a glass slide into a designated pattern is reported. The unwanted collagen-coated area was erased by a collagenase solution and the tailored area was retained by attaching a microfabricated polydimethylsiloxane stamp directly to the collagen-coated surface. Using this technique, collagen patterns with designated orientations and with clear pattern boundaries and defined shapes were fabricated.

FM-3DFR: Facial Manipulation-Based 3-D Face Reconstruction.

3-D Morphable model (3DMM) has widely benefited 3-D face-involved challenges given its parametric facial geometry and appearance representation. However, previous 3-D face reconstruction methods suffer from limited power in facial expression representation due to the unbalanced training data distribution and insufficient ground-truth 3-D shapes. In this article, we propose a novel framework to learn personalized shapes so that the reconstructed model well fits the corresponding face images. Specifically, we augment the dataset following several principles to balance the facial shape and expression distribution. A mesh editing method is presented as the expression synthesizer to generate more face images with various expressions. Besides, we improve the pose estimation accuracy by transferring the projection parameter into the Euler angles. Finally, a weighted sampling method is proposed to improve the robustness of the training process, where we define the offset between the base face model and the ground-truth face model as the sampling probability of each vertex. The experiments on several challenging benchmarks have demonstrated that our method achieves state-of-the-art performance.

Muscle Synergy Plasticity in Motor Function Recovery After Stroke.

In certain neurological disorders such as stroke, the impairment of upper limb function significantly impacts daily life quality and necessitates enhanced neurological control. This poses a formidable challenge in the realm of rehabilitation due to its intricate nature. Moreover, the plasticity of muscle synergy proves advantageous in assessing the enhancement of motor function among stroke patients pre and post rehabilitation training intervention, owing to the modular control strategy of central nervous system. It also facilitates the investigation of long-term alterations in remodeling of muscle functional performance among patients undergoing clinical rehabilitation, aiming to establish correlations between changes in muscle synergies and stroke characteristics such as type, stage, and sites. In this study, a three-week rehabilitation monitoring experiment was conducted to assess the motor function of stroke patients at different stages of rehabilitation based on muscle synergy performance. Additionally, we aimed to investigate the correlation between clinical scale scores, rehabilitation stages, and synergy performance in order to provide a more comprehensive understanding of stroke patient recovery. The results of 7 healthy controls and 16 stroke patients showed that high-functioning patients were superior to low-functioning patients in terms of motor function plasticity towards healthy individuals. Moreover, there was a high positive correlation between muscle synergies and clinical scale scores in high-functioning patients, and the significance gradually emerged with treatment, highlighting the potential of muscle synergy plasticity as a valuable tool for monitoring rehabilitation progress. The potential of this study was also demonstrated for elucidating the physiological mechanisms underlying motor function reconstruction within the central nervous system, which is expected to promote the further application of muscle synergy in clinical assessment.

Endoskeleton Soft Multi-Fingered Hand with Variable Stiffness.

The use of a soft multi-fingered hand in handling fragile objects has been widely acknowledged. Nevertheless, high flexibility often results in decreased load capacity, necessitating the need for variable stiffness. This article introduces a new soft multi-fingered hand featuring variable stiffness. The finger of the hand has three chambers and an endoskeleton mechanism. Two chambers facilitate bending and swinging motions, whereas the third adjusts stiffness. An endoskeleton mechanism is embedded in the third chamber, and the friction between its moving parts increases as negative air pressure rises, causing the finger's stiffness to increase. This mechanism can alter its stiffness in any configuration, which is particularly useful in manipulating irregular-shaped fragile objects post-grasping. The effectiveness of the proposed soft multi-fingered hand is validated through five experiments: stiffness adjustment, finger stiffening under a specific orientation, bulb screwing, heavy object lifting, and bean curd grasping. The results demonstrate that the proposed soft multi-fingered hand exhibits robust grasping capabilities for various fragile objects.

Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks.

OBJECTIVE: Volitional control systems for powered prostheses require the detection of user intent to operate in real life scenarios. Ambulation mode classification has been proposed to address this issue. However, these approaches introduce discrete labels to the otherwise continuous task that is ambulation. An alternative approach is to provide users with direct, voluntary control of the powered prosthesis motion. Surface electromyography (EMG) sensors have been proposed for this task, but poor signal-to-noise ratios and crosstalk from neighboring muscles limit performance. B-mode ultrasound can address some of these issues at the cost of reduced clinical viability due to the substantial increase in size, weight, and cost. Thus, there is an unmet need for a lightweight, portable neural system that can effectively detect the movement intention of individuals with lower-limb amputation. METHODS: In this study, we show that a small and lightweight A-mode ultrasound system can continuously predict prosthesis joint kinematics in seven individuals with transfemoral amputation across different ambulation tasks. Features from the A-mode ultrasound signals were mapped to the user's prosthesis kinematics via an artificial neural network. RESULTS: Predictions on testing ambulation circuit trials resulted in a mean normalized RMSE across different ambulation modes of 8.7 ± 3.1%, 4.6 ± 2.5%, 7.2 ± 1.8%, and 4.6 ± 2.4% for knee position, knee velocity, ankle position, and ankle velocity, respectively. CONCLUSION AND SIGNIFICANCE: This study lays the foundation for future applications of A-mode ultrasound for volitional control of powered prostheses during a variety of daily ambulation tasks.

Gaze Estimation by Attention-Induced Hierarchical Variational Auto-Encoder.

Appearance-based gaze estimation has been widely studied recently with promising performance. The majority of appearance-based gaze estimation methods are developed under the deterministic frameworks. However, the deterministic gaze estimation methods suffer from large performance drop upon challenging eye images in low-resolution, darkness, partial occlusions, etc. To alleviate this problem, in this article, we alternatively reformulate the appearance-based gaze estimation problem under a generative framework. Specifically, we propose a variational inference model, that is, variational gaze estimation network (VGE-Net), to generate multiple gaze maps as complimentary candidates simultaneously supervised by the ground-truth gaze map. To achieve robust estimation, we adaptively fuse the gaze directions predicted on these candidate gaze maps by a regression network through a simple attention mechanism. Experiments on three benchmarks, that is, MPIIGaze, EYEDIAP, and Columbia, demonstrate that our VGE-Net outperforms state-of-the-art gaze estimation methods, especially on challenging cases. Comprehensive ablation studies also validate the effectiveness of our contributions. The code will be publicly released.

A Generalized Cortico-Muscular-Cortical Network to Evaluate the Effects of Three-Week Brain Stimulation.

OBJECTIVE: Post-stroke transcranial magnetic stimulation (TMS) has gradually become a brain intervention to assist patients in the recovery of motor function. The long lasting regulatory of TMS may involve the coupling changes between cortex and muscles. However, the effects of multi-day TMS on motor recovery after stroke is unclear. METHODS: This study proposed to quantify the effects of three-week TMS on brain activity and muscles movement performance based on a generalized cortico-muscular-cortical network (gCMCN). The gCMCN-based features were further extracted and combined with the partial least squares (PLS) method to predict the Fugl-Meyer of upper extremity (FMUE) in stroke patients, thereby establishing an objective rehabilitation method that can evaluate the positive effects of continuous TMS on motor function. RESULTS: We found that the improvement of motor function after three-week TMS was significantly correlated with the complexity trend of information interaction between hemispheres and the intensity of corticomuscular coupling. In addition, the fitting coefficient ([Formula: see text]) for predicted and actual FMUE before and after TMS were 0.856 and 0.963, respectively, suggesting that the gCMCN-based measurement may be a promising method for evaluating the therapeutic effect of TMS. CONCLUSION: From the perspective of a novel brain-muscles network with dynamic contraction as the entry point, this work quantified TMS-induced connectivity differences while evaluating the potential efficacy of multi-day TMS. SIGNIFICANCE: It provides a unique insight for the further application of intervention therapy in the field of brain diseases.

Dual Regression-Enhanced Gaze Target Detection in the Wild.

Gaze is a vital feature in analyzing natural human behavior and social interaction. Existing gaze target detection studies learn gaze from gaze orientations and scene cues via a neural network to model gaze in unconstrained scenes. Though achieve decent accuracy, these studies either employ complex model architectures or leverage additional depth information, which limits the model application. This article proposes a simple and effective gaze target detection model that employs dual regression to improve detection accuracy while maintaining low model complexity. Specifically, in the training phase, the model parameters are optimized under the supervision of coordinate labels and corresponding Gaussian-smoothed heatmap labels. In the inference phase, the model outputs the gaze target in the form of coordinates as prediction rather than heatmaps. Extensive experimental results on within-dataset and cross-dataset evaluations on public datasets and clinical data of autism screening demonstrate that our model has high accuracy and inference speed with solid generalization capabilities.