Cyborg and bionic systems (Washington, D.C.)最新文献

Monocular 3D object detection plays a pivotal role in autonomous driving, presenting a formidable challenge by requiring the precise localization of 3D objects within a single image, devoid of depth information. Most existing methods in this domain fall short of harnessing the limited information available in monocular 3D detection tasks. They typically provide only a single detection outcome, omitting essential uncertainty analysis and result post-processing during model inference, thus limiting overall model performance. In this paper, we propose a comprehensive framework that maximizes information extraction from monocular images while encompassing diverse depth estimation and incorporating uncertainty analysis. Specifically, we mine additional information intrinsic to the monocular 3D detection task to augment supervision, thereby addressing the information scarcity challenge. Moreover, our framework handles depth estimation by recovering multiple sets of depth values from calculated visual heights. The final depth estimate and 3D confidence are determined through an uncertainty fusion process, effectively reducing inference errors. Furthermore, to address task weight allocation in multi-task training, we present a versatile training strategy tailored to monocular 3D detection. This approach leverages measurement indicators to monitor task progress, adaptively adjusting loss weights for different tasks. Experimental results on the KITTI and Waymo dataset confirm the effectiveness of our approach. The proposed method consistently provides enhanced performance across various difficulty levels compared to the original framework while maintaining real-time efficiency.

Everyday unnatural events such as trauma, accidents, military conflict, disasters, and even medical malpractice create open wounds and massive blood loss, which can be life-threatening. Fractures and large bone defects are among the most common types of injuries. Traditional treatment methods usually involve rapid hemostasis and wound closure, which are convenient and fast but may result in various complications such as nerve injury, deep infection, vascular injury, and deep hematomas. To address these complications, various studies have been conducted on new materials that can be degraded in the body and reduce inflammation and abscesses in the surgical area. This review presents the latest research progress in biomaterials for bone hemostasis and repair. The mechanisms of bone hemostasis and bone healing are first introduced and then principles for rational design of biomaterials are summarized. After providing representative examples of hemostatic biomaterials for bone repair, future challenges and opportunities in the field are proposed.

Biomachine hybrid robots have been proposed for important scenarios, such as wilderness rescue, ecological monitoring, and hazardous area surveying. The energy supply unit used to power the control backpack carried by these robots determines their future development and practical application. Current energy supply devices for control backpacks are mainly chemical batteries. To achieve self-powered devices, researchers have developed solar energy, bioenergy, biothermal energy, and biovibration energy harvesters. This review provides an overview of research in the development of chemical batteries and self-powered devices for biomachine hybrid robots. Various batteries for different biocarriers and the entry points for the design of self-powered devices are outlined in detail. Finally, an overview of the future challenges and possible directions for the development of energy supply devices used to biomachine hybrid robots is provided.

This paper presents a dynamic optical phantom for the simulation of metabolic activities in the brain, and a linear equivalent model is built for control voltage versus substance concentration. A solid-solid dynamic optical phantom is realized by using liquid crystal film as a voltage-controlled light intensity regulator on the surface of basic phantom, which uses epoxy resin as matrix material and nanometer carbon powder and titanium dioxide powder as absorption and scattering dopants, respectively. The dynamic phantom could mimic near-infrared spectrum (NIRS) signals with sampling rate up to 10 Hz, and the maximum simulation errors for oxy-hemoglobin and deoxy-hemoglobin concentrations varying in the range of 1 μmol/l are 7.0% and 17.9%, respectively. Compared with similar solid biomimetic phantoms, the adjustable mimic substance concentration range is extended by an order of magnitude, which meets the simulation requirements of most brain NIRS signals.

Machines that mimic humans have inspired scientists for centuries. Bioinspired soft robotic hands are a good example of such an endeavor, featuring intrinsic material compliance and continuous motion to deal with uncertainty and adapt to unstructured environments. Recent research led to impactful achievements in functional designs, modeling, fabrication, and control of soft robots. Nevertheless, the full realization of life-like movements is still challenging to achieve, often based on trial-and-error considerations from design to fabrication, consuming time and resources. In this study, a soft robotic hand is proposed, composed of soft actuator cores and an exoskeleton, featuring a multimaterial design aided by finite element analysis (FEA) to define the hand geometry and promote finger's bendability. The actuators are fabricated using molding, and the exoskeleton is 3D-printed in a single step. An ON-OFF controller keeps the set fingers' inner pressures related to specific bending angles, even in the presence of leaks. The FEA numerical results were validated by experimental tests, as well as the ability of the hand to grasp objects with different shapes, weights, and sizes. This integrated solution will make soft robotic hands more available to people, at a reduced cost, avoiding the time-consuming design-fabrication trial-and-error processes.

Dysfunctional blood vessels are implicated in various diseases, including cardiovascular diseases, neurodegenerative diseases, and cancer. Several studies have attempted to prevent and treat vascular diseases and understand interactions between these diseases and blood vessels across different organs and tissues. Initial studies were conducted using 2-dimensional (2D) in vitro and animal models. However, these models have difficulties in mimicking the 3D microenvironment in human, simulating kinetics related to cell activities, and replicating human pathophysiology; in addition, 3D models involve remarkably high costs. Thus, in vitro bioengineered models (BMs) have recently gained attention. BMs created through biofabrication based on tissue engineering and regenerative medicine are breakthrough models that can overcome limitations of 2D and animal models. They can also simulate the natural microenvironment in a patient- and target-specific manner. In this review, we will introduce 3D bioprinting methods for fabricating bioengineered blood vessel models, which can serve as the basis for treating and preventing various vascular diseases. Additionally, we will describe possible advancements from tubular to vascular models. Last, we will discuss specific applications, limitations, and future perspectives of fabricated BMs.

Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

In the last decade, organoids have gained popularity for developing mini-organs to support advancements in the study of organogenesis, disease modeling, and drug screening and, subsequently, in the development of new therapies. To date, such cultures have been used to replicate the composition and functionality of organs such as the kidney, liver, brain, and pancreas. However, depending on the experimenter, the culture environment and cell conditions may slightly vary, resulting in different organoids; this factor significantly affects their application in new drug development, especially during quantification. Standardization in this context can be achieved using bioprinting technology-an advanced technology that can print various cells and biomaterials at desired locations. This technology offers numerous advantages, including the manufacturing of complex three-dimensional biological structures. Therefore, in addition to the standardization of organoids, bioprinting technology in organoid engineering can facilitate automation in the fabrication process as well as a closer mimicry of native organs. Further, artificial intelligence (AI) has currently emerged as an effective tool to monitor and control the quality of final developed objects. Thus, organoids, bioprinting technology, and AI can be combined to obtain high-quality in vitro models for multiple applications.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current research on emotion recognition based on fNIRS is mainly limited to within-subject, and there is a lack of related work on emotion recognition across subjects. Therefore, in this paper, we designed an emotion evoking experiment with videos as stimuli and constructed the fNIRS emotion recognition database. On this basis, deep learning technology was introduced for the first time, and a dual-branch joint network (DBJNet) was constructed, creating the ability to generalize the model to new participants. The decoding performance obtained by the proposed model shows that fNIRS can effectively distinguish positive versus neutral versus negative emotions (accuracy is 74.8%, F1 score is 72.9%), and the decoding performance on the 2-category emotion recognition task of distinguishing positive versus neutral (accuracy is 89.5%, F1 score is 88.3%), negative versus neutral (accuracy is 91.7%, F1 score is 91.1%) proved fNIRS has a powerful ability to decode emotions. Furthermore, the results of the ablation study of the model structure demonstrate that the joint convolutional neural network branch and the statistical branch achieve the highest decoding performance. The work in this paper is expected to facilitate the development of fNIRS affective brain-computer interface.