Principles and methods of liquid metal actuators.

As a promising material, liquid metals (LMs) have gained considerable interest in the field of soft robotics due to their ability to move as designed routines or change their shape dramatically under external stimuli. Inspired by the science fiction film Terminator, tremendous efforts have been devoted to liquid robots with high compliance and intelligence. How to manipulate LM droplets is crucial to achieving this goal. Accordingly, this review is dedicated to presenting the principles driving LMs and summarizing the potential methods to develop LM actuators of high maneuverability. Moreover, the recent progress of LM robots based on these methods is overviewed. The challenges and prospects of implementing autonomous robots have been proposed.

Multi-channel EEG-based sleep staging using brain functional connectivity and domain adaptation.

Objective.Sleep stage recognition has essential clinical value for evaluating human physical/mental condition and diagnosing sleep-related diseases. To conduct a five-class (wake, N1, N2, N3 and rapid eye movement) sleep staging task, twenty subjects with recorded six-channel electroencephalography (EEG) signals from the ISRUC-SLEEP dataset is used.Approach.Unlike the exist methods ignoring the channel coupling relationship and non-stationarity characteristics, we developed a brain functional connectivity method to provide a new insight for multi-channel analysis. Furthermore, we investigated three frequency-domain features: two functional connectivity estimations, i.e. synchronization likelihood (SL) and wavelet-based correlation (WC) among four frequency bands, and energy ratio (ER) related to six frequency bands, respectively. Then, the Gaussian support vector machine (SVM) method was used to predict the five sleep stages. The performance of the applied features is evaluated in both subject dependence experiment by ten-fold cross validation and subject independence experiment by leave-one-subject-out cross-validation, respectively.Main results.In subject dependence experiment, the results showed that the fused feature (fusion of SL, WC and ER features) contributes significant gain the performance of SVM classifier, where the mean of classification accuracy can achieve 83.97% ± 1.04%. However, in subject-independence experiment, the individual differences EEG patterns across subjects leads to inferior accuracy. Five typical domain adaptation (DA) methods were applied to reduce the discrepancy of feature distributions by selecting the optimal subspace dimension. Results showed that four DA methods can significantly improve the mean accuracy by 1.89%-5.22% compared to the baseline accuracy 57.44% in leave-one-subject-out cross-validation.Significance.Compared with traditional time-frequency and nonlinear features, brain functional connectivity features can capture the correlation between different brain regions. For the individual EEG response differences, domain adaptation methods can transform features to improve the performance of sleep staging algorithms.

Evidence of synergy coordination patterns of upper-limb motor control in stroke patients with mild and moderate impairment.

Objectives: Previous studies showed that the central nervous system (CNS) controls movements by recruiting a low-dimensional set of modules, usually referred to as muscle synergies. Stroke alters the structure and recruitment patterns of muscle synergies, leading to abnormal motor performances. Some studies have shown that muscle synergies can be used as biomarkers for assessing motor function. However, coordination patterns of muscle synergies in post-stroke patients need more investigation to characterize how they are modified in functional movements. Methods: Thirteen mild-to-moderate stroke patients and twenty age-matched healthy subjects were recruited to perform two upper-limb movements, hand-to-mouth movement and reaching movement. Muscle synergies were extracted with nonnegative matrix factorization. We identified a set of reference synergies (i.e., averaged across healthy subjects) and typical synergies (i.e., averaged across stroke subjects) from the healthy group and stroke group respectively, and extracted affected synergies from each patient. Synergy similarity between groups was computed and analyzed. Synergy reconstruction analysis was performed to verify synergy coordination patterns in post-stroke patients. Results: On average, three synergies were extracted from both the healthy and stroke groups, while the mild impairment group had a significantly higher number of synergies than the healthy group. The similarity analysis showed that synergy structure was more consistent in the healthy group, and stroke instead altered synergy structure and induced more variability. Synergy reconstruction analysis at group and individual levels showed that muscle synergies of patients often showed a combination of healthy reference synergies in the analyzed movements. Finally, this study associated four synergy coordination patterns with patients: merging (equilibrium and disequilibrium), sharing (equilibrium and disequilibrium), losing, and preservation. The preservation was mainly represented in the mild impairment group, and the moderate impairment group showed more merging and sharing. Conclusion: This study concludes that stroke shows more synergy variability compared to the healthy group and the alterations of muscle synergies can be described as a combination of reference synergies by four synergy coordination patterns. These findings deepen the understanding of the underlying neurophysiological mechanisms and possible motor control strategies adopted by the CNS in post-stroke patients.

Structural biology and functional features of phage-derived depolymerase Depo32 on Klebsiella pneumoniae with K2 serotype capsular polysaccharides.

Hypervirulent Klebsiella pneumoniae with capsular polysaccharides (CPSs) causes severe nosocomial- and community-acquired infections. Phage-derived depolymerases can degrade CPSs from K. pneumoniae to attenuate bacterial virulence, but their antimicrobial mechanisms and clinical potential are not well understood. In the present study, Klebsiella phage GH-K3-derived depolymerase Depo32 (encoded by gene gp32) was identified to exhibit high efficiency in specifically degrading the CPSs of K2 serotype K. pneumoniae. The cryo-electron microscopy structure of trimeric Depo32 at a resolution up to 2.32 Å revealed potential catalytic centers in the cleft of each of the two adjacent subunits. K. pneumoniae subjected to Depo32 became more sensitive to phagocytosis by RAW264.7 cells and activated the cells by the mitogen-activated protein kinase signaling pathway. In addition, intranasal inoculation with Depo32 (a single dose of 200 µg, 20 µg daily for 3 days, or in combination with gentamicin) rescued all C57BL/6J mice infected with a lethal dose of K. pneumoniae K7 without interference from its neutralizing antibody. In summary, this work elaborates on the mechanism by which Depo32 targets the degradation of K2 serotype CPSs and its potential as an antivirulence agent. IMPORTANCE Depolymerases specific to more than 20 serotypes of Klebsiella spp. have been identified, but most studies only evaluated the single-dose treatment of depolymerases with relatively simple clinical evaluation indices and did not reveal the anti-infection mechanism of these depolymerases in depth. On the basis of determining the biological characteristics, the structure of Depo32 was analyzed by cryo-electron microscopy, and the potential active center was further identified. In addition, the effects of Depo32 on macrophage phagocytosis, signaling pathway activation, and serum killing were revealed, and the efficacy of the depolymerase (single treatment, multiple treatments, or in combination with gentamicin) against acute pneumonia caused by Klebsiella pneumoniae was evaluated. Moreover, the roles of the active sites of Depo32 were also elucidated in the in vitro and in vivo studies. Therefore, through structural biology, cell biology, and in vivo experiments, this study demonstrated the mechanism by which Depo32 targets K2 serotype K. pneumoniae infection.

Psychodynamic-based virtual reality cognitive training system with personalized emotional arousal elements for mild cognitive impairment patients.

BACKGROUND AND OBJECTIVE: Mild cognitive impairment (MCI) is a serious threat to the physical health and quality of life of the elderly, as well as a heavy burden on families and society. The current computer-based rehabilitation training ignores the role of emotions in cognitive impairment rehabilitation, making it difficult to improve patient engagement and efficiency. To address this, a psychodynamics-based cognitive rehabilitation training method with personalized emotional arousal elements was proposed using virtual reality technology. METHODS: Our proposed method contains four training tasks, which cover (audiovisual memory, attention & processing, working memory, abstract & Logic, spatial pathfinding) and six positive emotional arousal elements (sensory feedback, achievement system, multiplayer interaction, score comparison, relaxation scenarios, and peaceful videos) to motivate participants to persist during cognitive training continuously and maintain a positive mental attitude toward training. The six emotional arousal elements were divided into two personalized combinations-full combination and half combination-based on the results of the pre-assessment and were dynamically distributed throughout both the training tasks and post-training. RESULTS: Fifteen participants with MCI were recruited to complete the proposed experiment and validate the effectiveness of the system. They were first asked to complete two assessments (e.g., the big five scale and the positive and negative affect scale) to investigate their personalities. Based on the results of the assessments, they were provided with a full or half combination of arousal elements in the training tasks and post-training. Finally, the acceptability of the system and task experience were assessed using questionnaires. Notably, there was a significant increase in training scores for participants who completed a six-week training period (66.7%, 33.4%, and 25.0% for attention and processing, working memory, and abstraction and logic, respectively). The results show that positive emotional arousal had a positive effect on the MCI participants. The training tasks and arousal elements can improve cognitive function and enhance the confidence and engagement of participants. There were no significant differences in cognitive domain training scores between the two groups. CONCLUSIONS: This personalized cognitive training system has the potential to serve as a convenient solution for complementary treatment of MCI.

Prevalence and risk factors of Klebsiella spp. in milk samples from dairy cows with mastitis-A global systematic review.

Introduction: The overall prevalence of Klebsiella spp., a group of important zoonotic pathogens, in the global dairy herds and the risk of cross-species transmission between humans and dairy cows remain to be clarified. This systematic review aimed to determine the prevalence of Klebsiella spp. in milk samples from dairy cows with mastitis worldwide and to assess the factors influencing the prevalence of these strains. Methods: Qualified studies published from 2007 to 2021 were retrieved from ScienceDirect, Web of Science, PubMed, WanFang Database, China National Knowledge Infrastructure (CNKI), and VIP Chinese Journal Database. Calculations of prevalence and their 95% confidence intervals (CIs) were performed for all the studies using the Freeman-Tukey double arcsine transformation (PFT). Results: A total of 79,852 milk samples from 55 manuscripts were examined in this meta-analysis, and 2,478 samples were found to be positive for Klebsiella spp. The pooled prevalence estimates worldwide were 7.95% (95% CI: 6.07%-10.06%), with significant heterogeneity (I 2 = 98.8%, p = 0). The sampling period of 2013-2020 had a higher (p < 0.05) Klebsiella-positive proportion of milk samples (12.16%, 95% CI: 8.08%-16.90%) than that of 2007-2012 (3.85%, 95% CI: 2.67%-5.21%), indicating that bovine mastitis caused by Klebsiella may become increasingly prevalent. The risk factors for the high prevalence of Klebsiella in milk samples mainly included: economic development level (developing countries; 11.76%, 95% CI: 8.25%-15.77%), mastitis type (CM; 11.99%, 95% CI: 8.62%-15.79%), and population density (>500 per sq km; 10.28%, 95% CI: 2.73%-21.58%). Additionally, a bivariate meta-regression analysis revealed that the multidrug-resistance (MDR) rate of the epidemic strains was also closely related to economic development level (R 2 = 78.87%) and population density (R 2 = 87.51%). Discussion: Due to the potential risk of cross-species transmission between humans and cows, the prevalence of mastitis milk-derived Klebsiella and its high MDR rate need to be monitored, especially in developing countries with high population densities.

Electronic whiskers for velocity sensing based on the liquid metal hysteresis effect.

The artificial biomimetic sensory hair as state-of-art electronics has drawn great attention from academic theorists of industrial production given its potential application in soft robotics, environmental exploration and health monitoring. However, it still remains a challenge to develop highly sensitive electronic sensory hair with fast response. In this study, a bio-inspired electronic whisker (e-whisker) with a hollow polymer shell and a liquid metal core was prepared by microinjection for airflow measurement and detection of obstacles. In addition, we illustrated the effect of liquid metal hysteresis on its distribution in microchannels on deformation. The difference in the deformed velocity between the selected fiber and EGaIn would result in a disturbance emerging in the liquid metal channel, which further causes a variation in resistance. Taking advantage of this phenomenon, the integrated fiber e-whisker can be employed to detect tiny airflow and disturbance. The experimental results indicate that the fiber sensor can detect the airflow velocity as low as 0.2 m s-1 within 0.1 s. The e-whisker can accurately monitor rainfall, human motion and object velocity. This work sheds light on the liquid metal viscosity-induced sensing mechanism and offers a novel strategy to fabricate high-performance velocity sensors.

Investigation of two neural mass models for DCM-based effective connectivity inference in temporal epilepsy.

BACKGROUND AND OBJECTIVE: Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around ß and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned. METHODS: To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM. RESULTS: The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method. CONCLUSIONS: Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.

Sources and processes of organic aerosol in non-refractory PM1 and PM2.5 during foggy and haze episodes in an urban environment of the Yangtze River Delta, China.

Organic aerosol (OA) generally accounts for a large fraction of fine particulate matter (PM2.5) in the urban atmosphere. Despite significant advances in the understanding their emission sources, transformation processes and optical properties in the submicron aerosol fraction (PM1), larger size fractions - e.g., PM2.5 - still deserve complementary investigations. In this study, we conducted a comprehensive analysis on sources, formation process and optical properties of OA in PM1 and PM2.5 under haze and foggy environments in the Yangtze River Delta (eastern China), using two aerosol chemical speciation monitors, as well as a photoacoustic extinctiometer at 870 nm. Positive matrix factorization analysis - using multilinear engine (ME2) algorithm - was conducted on PM1 and PM2.5 organic mass spectra. Four OA factors were identified, including three primary OA (POA) factors, i.e., hydrocarbon-like OA (HOA), cooking OA (COA), and biomass burning OA (BBOA), and a secondary OA (SOA) factor, i.e., oxidized oxygenated OA (OOA). An enhanced PM1-2.5 COA concentration was clearly observed during cooking peak hours, suggesting important contribution of fresh cooking emissions on large-sized particles (i.e., PM1-2.5). The oxidation state and concentration of PM2.5 HOA were higher than that in PM1, suggesting that large-sized HOA particles might be linked to oxidized POA. High contribution (44%) of large-sized OOA to non-refractory PM2.5 mass was observed during haze episodes. During foggy episodes, PM1 and PM2.5 OOA concentrations increased as a positive relationship over time, along with an exponential increase in the PM2.5-OOA to PM1-OOA ratio. Meanwhile, OOA loadings increased with the aerosol liquid water content (ALWC) during foggy episodes. Random forest cross-validation analysis also supported the important influence of ALWC on OOA variations, supporting substantial impact of aqueous process on SOA formation during haze and/or foggy episodes. Obtained results also indicated high OOA contributions (21%-36%) and low POA contributions (6%-14%) to the PM2.5 scattering coefficient during haze and foggy episodes, respectively. Finally, we could illustrate that atmospheric vertical diffusion and horizontal transport have important but different effects on the concentrations of different primary and secondary OA factors in different particle size fractions.

LatLRR-FCNs: Latent Low-Rank Representation With Fully Convolutional Networks for Medical Image Fusion.

Medical image fusion, which aims to derive complementary information from multi-modality medical images, plays an important role in many clinical applications, such as medical diagnostics and treatment. We propose the LatLRR-FCNs, which is a hybrid medical image fusion framework consisting of the latent low-rank representation (LatLRR) and the fully convolutional networks (FCNs). Specifically, the LatLRR module is used to decompose the multi-modality medical images into low-rank and saliency components, which can provide fine-grained details and preserve energies, respectively. The FCN module aims to preserve both global and local information by generating the weighting maps for each modality image. The final weighting map is obtained using the weighted local energy and the weighted sum of the eight-neighborhood-based modified Laplacian method. The fused low-rank component is generated by combining the low-rank components of each modality image according to the guidance provided by the final weighting map within pyramid-based fusion. A simple sum strategy is used for the saliency components. The usefulness and efficiency of the proposed framework are thoroughly evaluated on four medical image fusion tasks, including computed tomography (CT) and magnetic resonance (MR), T1- and T2-weighted MR, positron emission tomography and MR, and single-photon emission CT and MR. The results demonstrate that by leveraging the LatLRR for image detail extraction and the FCNs for global and local information description, we can achieve performance superior to the state-of-the-art methods in terms of both objective assessment and visual quality in some cases. Furthermore, our method has a competitive performance in terms of computational costs compared to other baselines.

[Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike's Information Criterion].

The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike's information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.

A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures.

This paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. The performance of the proposed strategy in terms of effective connectivity identification is assessed using simulated data generated by a neuronal mass model (simulating unidirectional and bidirectional flows) and real epileptic intracerebral Electroencephalographic signals. Results show the efficiency of proposed approach compared to the conventional Dynamic Causal Modelling and the one wherein a deterministic annealing scheme is employed.