Molecular mechanisms underlying human spatial cognitive ability revealed with neurotransmitter and transcriptomic mapping.

Mental rotation, one of the cores of spatial cognitive abilities, is closely associated with spatial processing and general intelligence. Although the brain underpinnings of mental rotation have been reported, the cellular and molecular mechanisms remain unexplored. Here, we used magnetic resonance imaging, a whole-brain spatial distribution atlas of 19 neurotransmitter receptors, transcriptomic data from Allen Human Brain Atlas, and mental rotation performances of 356 healthy individuals to identify the genetic/molecular foundation of mental rotation. We found significant associations of mental rotation performance with gray matter volume and fractional amplitude of low-frequency fluctuations in primary visual cortex, fusiform gyrus, primary sensory-motor cortex, and default mode network. Gray matter volume and fractional amplitude of low-frequency fluctuations in these brain areas also exhibited significant sex differences. Importantly, spatial correlation analyses were conducted between the spatial patterns of gray matter volume or fractional amplitude of low-frequency fluctuations with mental rotation and the spatial distribution patterns of neurotransmitter receptors and transcriptomic data, and identified the related genes and neurotransmitter receptors associated with mental rotation. These identified genes are localized on the X chromosome and are mainly involved in trans-synaptic signaling, transmembrane transport, and hormone response. Our findings provide initial evidence for the neural and molecular mechanisms underlying spatial cognitive ability.

Optical and MRI Multimodal Tracing of Stem Cells In Vivo.

Stem cell therapy has shown great clinical potential in oncology, injury, inflammation, and cardiovascular disease. However, due to the technical limitations of the in vivo visualization of transplanted stem cells, the therapeutic mechanisms and biosafety of stem cells in vivo are poorly defined, which limits the speed of clinical translation. The commonly used methods for the in vivo tracing of stem cells currently include optical imaging, magnetic resonance imaging (MRI), and nuclear medicine imaging. However, nuclear medicine imaging involves radioactive materials, MRI has low resolution at the cellular level, and optical imaging has poor tissue penetration in vivo. It is difficult for a single imaging method to simultaneously achieve the high penetration, high resolution, and noninvasiveness needed for in vivo imaging. However, multimodal imaging combines the advantages of different imaging modalities to determine the fate of stem cells in vivo in a multidimensional way. This review provides an overview of various multimodal imaging technologies and labeling methods commonly used for tracing stem cells, including optical imaging, MRI, and the combination of the two, while explaining the principles involved, comparing the advantages and disadvantages of different combination schemes, and discussing the challenges and prospects of human stem cell tracking techniques.

A collaborative robot for COVID-19 oropharyngeal swabbing.

The coronavirus disease 2019 (COVID-19) outbreak has increased mortality and morbidity world-wide. Oropharyngeal swabbing is a well-known and commonly used sampling technique for COVID-19 diagnose around the world. We developed a robot to assist with COVID-19 oropharyngeal swabbing to prevent frontline clinical staff from being infected. The robot integrates a UR5 manipulator, rigid-flexible coupling (RFC) manipulator, force-sensing and control subsystem, visual subsystem and haptic device. The robot has strength in intrinsically safe and high repeat positioning accuracy. In addition, we also achieve one-dimensional constant force control in the automatic scheme (AS). Compared with the rigid sampling robot, the developed robot can perform the oropharyngeal swabbing procedure more safely and gently, reducing risk. Alternatively, a novel robot control schemes called collaborative manipulation scheme (CMS) which combines a automatic phase and teleoperation phase is proposed. At last, comparative experiments of three schemes were conducted, including CMS, AS, and teleoperation scheme (TS). The experimental results shows that CMS obtained the highest score according to the evaluation equation. CMS has the excellent performance in quality, experience and adaption. Therefore, the proposal of CMS is meaningful which is more suitable for robot-sampling.

Maximum Correntropy with Variable Center Unscented Kalman Filter for Robust Power System State Estimation.

The robust Kalman filter with correntropy loss has received much attention in recent years for forecasting-aided state estimation in power systems, since it efficiently reduces the negative influence of various abnormal situations, such as non-Gaussian communication, changing environment, and instrument failures, and obviously improves the stability of power systems. However, the existing correntropy-based robust Kalman filters usually use the Gaussian function with a fixed center as the kernel function in correntropy, which may not be a suitable choice in practical applications of power system forecasting-aided state estimation (PSSE). To address this issue, a new and robust unscented Kalman filter, called the maximum correntropy with variable center unscented Kalman filter (MCVUKF), is proposed in this paper for PSSE. Specifically, MCVUKF adopts an extended version of correntropy, whose center can be located at any position, to replace the original correntropy in an unscented Kalman filter to improve the performance in PSSE. Moreover, by using an exponential function of the innovation vector to adjust a covariance matrix, an enhanced MCVUKF (En-MCVUKF) method is also developed for suppressing the influence of bad data to the innovation vector and further improving the accuracy of PSSE. Finally, extensive simulations have been conducted on IEEE 14-bus, 30-bus, and 57-bus test power systems, and the simulation results have shown the superiority of the proposed MCVUKF and En-MCVUKF methods compared with several related state-of-the-art Kalman filter methods.

Study of growth, metabolism, and morphology of Akkermansia muciniphila with an in vitro advanced bionic intestinal reactor.

BACKGROUND: As a kind of potential probiotic, Akkermansia muciniphila abundance in human body is directly causally related to obesity, diabetes, inflammation and abnormal metabolism. In this study, A. muciniphila dynamic cultures using five different media were implemented in an in vitro bionic intestinal reactor for the first time instead of the traditional static culture using brain heart infusion broth (BHI) or BHI + porcine mucin (BPM). RESULTS: The biomass under dynamic culture using BPM reached 1.92 g/L, which improved 44.36% compared with the value under static culture using BPM. The biomass under dynamic culture using human mucin (HM) further increased to the highest level of 2.89 g/L. Under dynamic culture using porcine mucin (PM) and HM, the main metabolites were short-chain fatty acids (acetic acid and butyric acid), while using other media, a considerable amount of branched-chain fatty acids (isobutyric and isovaleric acids) were produced. Under dynamic culture Using HM, the cell diameters reached 999 nm, and the outer membrane protein concentration reached the highest level of 26.26 µg/mg. CONCLUSIONS: This study provided a preliminary theoretical basis for the development of A. muciniphila as the next generation probiotic.

Estimation of Foot Plantar Center of Pressure Trajectories with Low-Cost Instrumented Insoles Using an Individual-Specific Nonlinear Model.

Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, the foot plantar COP was mainly measured by force plates or complicated and expensive insole-based measurement systems. Although some low-cost instrumented insoles have been developed, their ability to accurately estimate the foot plantar COP trajectory was not robust. In this study, a novel individual-specific nonlinear model was proposed to estimate the foot plantar COP trajectories with an instrumented insole based on low-cost force sensitive resistors (FSRs). The model coefficients were determined by a least square error approximation algorithm. Model validation was carried out by comparing the estimated COP data with the reference data in a variety of postural control assessment tasks. We also compared our data with the COP trajectories estimated by the previously well accepted weighted mean approach. Comparing with the reference measurements, the average root mean square errors of the COP trajectories of both feet were 2.23 mm (±0.64) (left foot) and 2.72 mm (±0.83) (right foot) along the medial-lateral direction, and 9.17 mm (±1.98) (left foot) and 11.19 mm (±2.98) (right foot) along the anterior-posterior direction. The results are superior to those reported in previous relevant studies, and demonstrate that our proposed approach can be used for accurate foot plantar COP trajectory estimation. This study could provide an inexpensive solution to fall risk assessment in home settings or community healthcare center for the elderly. It has the potential to help prevent future falls in the elderly.

High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis.

In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison.

A DEAD-box RNA helicase produces two forms of transcript that differentially respond to cold stress in a cryophyte (Chorispora bungeana).

MAIN CONCLUSION: This work demonstrated that a cold-induced DEAD-box RNA helicase, CbDRH, is also post-transcriptionally regulated upon cold stress, and it interacts with a cold-responsive, glycine-rich, RNA-binding protein, CbGRP. Chorispora bungeana (C. bungeana) is a representative alpine subnival plant species that shows strong tolerance to multiple abiotic stresses, especially cold stress. DEAD-box RNA helicases are implicated in almost all RNA metabolic processes and participate in multiple abiotic stress responses. Here, we characterized a cold-induced DEAD-box RNA helicase gene from C. bungeana. We cloned the full-length cDNA of the gene by RACE and called it C. bungeana DEAD-box RNA Helicase (CbDRH). Structurally, CbDRH possesses all nine conserved motifs characteristic of DEAD-box protein family members in its central region, and the N- and C- terminal extensions both harbor a glycine-rich region containing several RGG-box motifs. The CbDRH gene produces two forms of transcripts, CbDRH.2 and CbDRH.1, by alternative splicing. CbDRH.2 comes from the complete excision of all the nine introns, while CbDRH.1 results from the use of an alternative 5' splice site in the eighth intron, retaining part of the intron (the first 260 bp) with an early stop codon. Semi-quantitative RT-PCR analysis showed that CbDRH.2, but not CbDRH.1, is up-regulated by cold stress. However, the abundance of CbDRH.1 transcript can be elevated by cycloheximide (an inhibitor of nonsense-mediated decay) treatment, indicating that CbDRH.1 is targeted to nonsense-mediated decay (NMD). A subcellular localization analysis showed that CbDRH.2 protein is located in the nuclei. Further investigation suggested that CbDRH.2 can interact with a cold-responsive, glycine-rich, RNA-binding protein, CbGRP (Chorispora bungeana glycine-rich, RNA-binding protein). These data suggest that the cold-induced CbDRH is also post-transcriptionally regulated under cold stress and that CbDRH.2 may function together with the glycine-rich, RNA-binding protein, CbGRP, in the cold stress response.

A Low-Cost Instrumented Shoe System for Gait Phase Detection Based on Foot Plantar Pressure Data.

This paper presents a novel low-cost and fully-portable instrumented shoe system for gait phase detection. The instrumented shoe consists of 174 independent sensing units constructed based on an off-the-shelf force-sensitive film known as the Velostat conductive copolymer. A zero potential method was implemented to address the crosstalk effect among the matrix-formed sensing arrays. A customized algorithm for gait event and phase detection was developed to estimate stance sub-phases including initial contact, flat foot, and push off. Experiments were carried out to evaluate the performance of the proposed instrumented shoe system in gait phase detection for both straight-line walking and turning walking. The results showed that the mean absolute time differences between the estimated phases by the proposed instrumented shoe system and the reference measurement ranged from 45 to 58 ms during straight-line walking and from 51 to 77 ms during turning walking, which were comparable to the state of art.Clinical and Translational Impact Statement-By allowing convenient gait monitoring in home healthcare settings, the proposed system enables extensive ADL data collection and facilitates developing effective treatment and rehabilitation strategies for patients with movement disorders.

Teaching Masked Autoencoder With Strong Augmentations.

Masked autoencoder (MAE) has been regarded as a capable self-supervised learner for various downstream tasks. Nevertheless, the model still lacks high-level discriminability, which results in poor linear probing performance. In view of the fact that strong augmentation plays an essential role in contrastive learning, can we capitalize on strong augmentation in MAE? The difficulty originates from the pixel uncertainty caused by strong augmentation that may affect the reconstruction, and thus, directly introducing strong augmentation into MAE often hurts the performance. In this article, we delve into the potential of strong augmented views to enhance MAE while maintaining MAE's advantages. To this end, we propose a simple yet effective masked Siamese autoencoder (MSA) model, which consists of a student branch and a teacher branch. The student branch derives MAE's advanced architecture, and the teacher branch treats the unmasked strong view as an exemplary teacher to impose high-level discrimination onto the student branch. We demonstrate that our MSA can improve the model's spatial perception capability and, therefore, globally favors interimage discrimination. Empirical evidence shows that the model pretrained by MSA provides superior performances across different downstream tasks. Notably, linear probing performance on frozen features extracted from MSA leads to 6.1% gains over MAE on ImageNet-1k. Fine-tuning (FT) the network on VQAv2 task finally achieves 67.4% accuracy, outperforming 1.6% of the supervised method DeiT and 1.2% of MAE. Codes and models are available at https://github.com/KimSoybean/MSA.

Self-Adaptive Perception of Object's Deformability with Multiple Deformation Attributes Utilizing Biomimetic Mechanoreceptors.

The perception of object's deformability in unstructured interactions relies on both kinesthetic and cutaneous cues to adapt the uncertainties of an object. However, the existing tactile sensors cannot provide adequate cutaneous cues to self-adaptively estimate the material softness, especially in non-standard contact scenarios where the interacting object deviates from the assumption of an elastic half-infinite body. This paper proposes an innovative design of a tactile sensor that integrates the capabilities of two slow-adapting mechanoreceptors within a soft medium, allowing self-decoupled sensing of local pressure and strain at specific locations within the contact interface. By leveraging these localized cutaneous cues, the sensor can accurately and self-adaptively measure the material softness of an object, accommodating variations in thicknesses and applied forces. Furthermore, when combined with a kinesthetic cue from the robot, the sensor can enhance tactile expression by the synergy of two relevant deformation attributes, including material softness and compliance. It is demonstrated that the biomimetic fusion of tactile information can fully comprehend the deformability of an object, hence facilitating robotic decision-making and dexterous manipulation.

A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner.

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain; with proper path planning, it can also minimize the potential damage by setting constraints and optimizing the insertion path. Recently, reinforcement learning (RL)-based path planning algorithm has shown promising results in neurosurgery, but because of the trial and error mechanism, it can be computationally expensive and insecure with low training efficiency. In this paper, we propose a heuristically accelerated deep Q network (DQN) algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment. Furthermore, a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm. Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms. Tests showed promising results of our algorithm in saving over 50 training episodes, calculating path lengths of 0.35 after normalization, which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm, respectively. Moreover, the maximum curvature during planning is reduced to 0.046 from 0.139 mm-1 using the proposed algorithm compared to DQN.

Characteristics of foot plantar pressure during turning in young male adults.

BACKGROUND: Turning gait is considered as a challenging motor task. However, only few existing studies reported turning biomechanics from the aspect of foot plantar pressure. RESEARCH QUESTION: This study aimed to investigate turning biomechanics by studying foot plantar pressure characteristics METHODS: Twelve young male participants were involved in this experimental study. They were instructed to perform turning tasks with different turning angles (i.e., 30°, 60°, and 90°). Foot plantar pressure was quantified by the force time integral (FTI) underneath seven plantar sub-areas. Analysis was carried out for different turning strategies (spin turns versus step turns), separately. RESULTS: The results showed that for small-angle spin turns, plantar pressure patterns changed at the early stage of the approaching step, suggesting a preparatory action for the increased lower limb range of motion in the transverse plane during turning; for step turns, an imbalance weight bearing mechanism was adopted when making large-angle turns to compensate for the centripetal force during turning. SIGNIFICANCE: The findings provide improved knowledge about turning biomechanics. They have practical implications for motion planning of lower-limb assistive devices for those with difficulties in turning.

Surgical Continuum Manipulator Control Using Multiagent Team Deep Q Learning.

Continuum manipulator has shown great potential in surgical applications. The flexibility of the continuum manipulator helps it achieve many complicated surgeries, such as neurosurgery, vascular surgery, abdominal surgery, etc. In this paper, we propose a Team Deep Q learning framework (TDQN) to control a 2-DoF surgical continuum manipulator with four cables, where two cables in a pair form one agent. During the learning process, each agent shares state and reward information with the other one, which namely is centralized learning. Using the shared information, TDQN shows better targeting accuracy than multiagent deep Q learning (MADQN) by verifying on a 2-DoF cable-driven surgical continuum manipulator. The root mean square error during tracking with and without disturbance are 0.82mm and 0.16mm respectively using TDQN, whereas 1.52mm and 0.98mm using MADQN respectively.Clinical Relevance-The proposed TDQN shows a promising future in improving control accuracy under disturbance and maneuverability in robotic-assisted endoscopic surgery.

Training Induced Improvement on Human Perception of the Force Feedback Provided by A Soft Robotic Glove.

Creating haptic interface by glove-based wearable robotic system has become an increasingly interested topic in the area of human robotic interaction. Many force feedback gloves are constructed based on soft actuators. However, the recent development of haptic and force feedback technology mainly focused on the advancement of the actuating components and mechanism, and innovation of the force feedback rendering algorithms. It seems that another important part of this human-robot-interaction loop, i.e. the human factors, were understudied. Here, this study focused on the learning effect in haptic perception. We designed a pneumatic muscle-based force feedback robotic glove which can provide customized force feedback to the dorsal surface of each finger. An experiment was carried out with a specifically training procedure and evaluation on the force feedback perceptions. The results show that practice-induced improvement can be achieved by this training, allowing people have a better perception of the force feedback provided by this glove.

Road Narrow-Inspired Strain Concentration to Wide-Range-Tunable Gauge Factor of Ionic Hydrogel Strain Sensor.

The application of stretchable strain sensors in human movement recognition, health monitoring, and soft robotics has attracted wide attention. Compared with traditional electronic conductors, stretchable ionic hydrogels are more attractive to organization-like soft electronic devices yet suffer poor sensitivity due to limited ion conduction modulation caused by their intrinsic soft chain network. This paper proposes a strategy to modulate ion transport behavior by geometry-induced strain concentration to adjust and improve the sensitivity of ionic hydrogel-based strain sensors (IHSS). Inspired by the phenomenon of vehicles slowing down and changing lanes when the road narrows, the strain redistribution of ionic hydrogel is optimized by structural and mechanical parameters to produce a strain-induced resistance boost. As a result, the gauge factor of the IHSS is continuously tunable from 1.31 to 9.21 in the strain range of 0-100%, which breaks through the theoretical limit of homogeneous strain-distributed ionic hydrogels and ensures a linear electromechanical response simultaneously. Overall, this study offers a universal route to modulate the ion transport behavior of ionic hydrogels mechanically, resulting in a tunable sensitivity for IHSS to better serve different application scenarios, such as health monitoring and human-machine interface.

Robot-assisted endoscopic submucosal dissection is effective in treating patients with early-stage gastric neoplasia.

BACKGROUND & AIMS: Endoscopic submucosal dissection (ESD) is a new technique for endoscopic resection of early-stage gastrointestinal cancers. Though ESD achieves high rate of en bloc resection, it is technically difficult to master. The development of a novel robotic endoscopic system that has 2 arms attached to an ordinary endoscope-Master and Slave Transluminal Endoscopic Robot (MASTER)-has improved the performance of complex endoluminal procedures. We evaluated the efficacy of MASTER-assisted ESD in treatment of patients with early-stage gastric neoplasia. METHODS: We performed a multicenter prospective study of 5 patients with early-stage gastric neoplasia, limited to the mucosa. After markings and circumferential mucosal incision, all submucosal dissections were performed using the MASTER system. We measured baseline demographics, tumor characteristics, and perioperative and clinical outcomes. RESULTS: All patients underwent successful MASTER-assisted ESD. The mean submucosal dissection time was 18.6 minutes (median, 16 minutes; range, 3-50 minutes). No perioperative complications were encountered. Three patients were discharged from the hospital within 12 hours and 2 on the third day after the procedures. Two patients were found to have intramucosal adenocarcinoma, 1 had high-grade dysplasia, 1 had low-grade dysplasia, and 1 had a hyperplastic polyp. The resection margins were clear of tumors in all 5 patients. No complications were observed at the 30-day follow-up examination. Follow-up endoscopic examinations revealed that none of the patients had residual or recurrent tumors. CONCLUSIONS: A flexible endoscopy robotic system can be used to perform ESD and effectively treat patients with early gastric neoplasia.

Recurrent neural networks as kinematics estimator and controller for redundant manipulators subject to physical constraints.

Redundant manipulators could be efficient tools in industrial production as a result of their dexterity. However, existing kinematic control methods for redundant manipulators have two main disadvantages. On one hand, model uncertainties or unknown kinematic parameters may degrade the performance of existing model-based control methods subject to joint limits. On the other hand, existing model-free control methods ignore the existence of joint limits although they do not need to know kinematic models of redundant manipulators. In this paper, a quadratic programming (QP) scheme is elaborated to achieve the primary tracking control task of redundant manipulators as well as joint limits avoidance task. Besides, a gradient neurodynamics (GND) model is utilized to estimate the kinematics of redundant manipulators. Then, a primal dual neural network, which is employed to solve the QP problem, and the GND model are integrated towards developing a model-free control method constrained by joint angle and velocity limits for redundant manipulators. The visual sensory feedback is fed to the two neural networks. The efficacy of the proposed control method is demonstrated by extensive simulations and experiments, and the merits of the proposed method are also substantiated by comparisons.

Mechanically Interlocked Hydrogel-Elastomer Strain Sensor with Robust Interface and Enhanced Water-Retention Capacity.

Hydrogels are stretchable ion conductors that can be used as strain sensors by transmitting strain-dependent electrical signals. However, hydrogels are susceptible to dehydration in the air, leading to a loss of flexibility and functions. Here, a simple and general strategy for encapsulating hydrogel with hydrophobic elastomer is proposed to realize excellent water-retention capacity. Elastomers, such as polydimethylsiloxanes (PDMS), whose hydrophobicity and dense crosslinking network can act as a barrier against water evaporation (lost 4.6 wt.% ± 0.57 in 24 h, 28 °C, and ≈30% humidity). To achieve strong adhesion between the hydrogel and elastomer, a porous structured thermoplastic polyurethane (TPU) is used at the hydrogel-elastomer interface to interlock the hydrogel and bond the elastomer simultaneously (the maximum interfacial toughness is over 1200 J/m2). In addition, a PDMS encapsulated ionic hydrogel strain sensor is proposed, demonstrating an excellent water-retention ability, superior mechanical performance, highly linear sensitivity (gauge factor = 2.21, at 100% strain), and robust interface. Various human motions were monitored, proving the effectiveness and practicability of the hydrogel-elastomer hybrid.

Design and Control of a Highly Redundant Rigid-flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling.

The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF) rigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling. This robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The robot is expected to reduce risk and free up the clinical staff from the long-term repetitive sampling work. Compared with a rigid sampling robot, the developed force-sensing RFC robot can facilitate OP-swab sampling procedures in a safer and softer way. In addition, a varying-parameter zeroing neural network-based optimization method is also proposed for motion planning of the 9-DOF redundant manipulator. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.