Clinical and neuropsychological correlates of theta-band functional excitation-inhibition ratio in autism: An EEG study.

OBJECTIVE: How abnormal brain signaling impacts cognition in autism spectrum disorder (ASD) remained elusive. This study aimed to investigate the local and global brain signaling in ASD indicated by theta-band functional excitation-inhibition (fE/I) ratio and explored psychophysiological relationships between fE/I, cognitive deficits, and ASD symptomatology. METHODS: A total of 83 ASD and typically developing (TD) individuals participated in this study. Participants' interference control and set-shifting abilities were assessed. Resting-state electroencephalography (EEG) was used for estimating theta-band fE/I ratio. RESULTS: ASD individuals (n = 31 without visual EEG abnormality; n = 22 with visual EEG abnormality) generally performed slower in a cognitive task tapping interference control and set-maintenance abilities, but only ASD individuals with visually abnormal EEG performed significantly slower than their TD counterparts (Bonferroni-corrected ps < .001). Heightened theta-band fE/I ratios at the whole-head level, left and right hemispheres were observed in the ASD subgroup without visual EEG abnormality only (Bonferroni-corrected ps < .001), which remained highly significant when only data from medication-naïve participants were analyzed. In addition, higher left hemispheric fE/I ratios in ASD individuals without visual EEG abnormality were significantly correlated with faster interference control task performance, in turn faster reaction time was significantly associated with less severe restricted, repetitive behavior (Bonferroni-corrected ps ≤ .0017). CONCLUSIONS: Differential theta-band fE/I within the ASD population. Heightened theta-band fE/I in ASD without visual EEG abnormality may be associated with more efficient filtering of distractors and a less severe ASD symptom manifestation. SIGNIFICANCE: Brain signaling, indicated by theta-band fE/I, was different in ASD subgroups. Only ASD with visually-normal EEG showed heightened theta-band fE/I, which was associated with faster processing of visual distractors during a cognitive task. More efficient distractor filtering was associated with less restricted, repetitive behaviors.

Role of neutrophils on cancer cells and other immune cells in the tumor microenvironment.

The notion that neutrophils only perform a specific set of single functions in the body has changed with the advancement of research methods. As the most abundant myeloid cells in human blood, neutrophils are currently emerging as important regulators of cancer. Given the duality of neutrophils, neutrophil-based tumor therapy has been clinically carried out in recent years and has made some progress. But due to the complexity of the tumor microenvironment, the therapeutic effect is still not satisfactory. Therefore, in this review, we discuss the direct interaction of neutrophils with the five most common cancer cells and other immune cells in the tumor microenvironment. Also, this review covered current limitations, potential future possibilities, and therapeutic approaches targeting neutrophil function in cancer therapy.

A multimodal human-robot sign language interaction framework applied in social robots.

Deaf-mutes face many difficulties in daily interactions with hearing people through spoken language. Sign language is an important way of expression and communication for deaf-mutes. Therefore, breaking the communication barrier between the deaf-mute and hearing communities is significant for facilitating their integration into society. To help them integrate into social life better, we propose a multimodal Chinese sign language (CSL) gesture interaction framework based on social robots. The CSL gesture information including both static and dynamic gestures is captured from two different modal sensors. A wearable Myo armband and a Leap Motion sensor are used to collect human arm surface electromyography (sEMG) signals and hand 3D vectors, respectively. Two modalities of gesture datasets are preprocessed and fused to improve the recognition accuracy and to reduce the processing time cost of the network before sending it to the classifier. Since the input datasets of the proposed framework are temporal sequence gestures, the long-short term memory recurrent neural network is used to classify these input sequences. Comparative experiments are performed on an NAO robot to test our method. Moreover, our method can effectively improve CSL gesture recognition accuracy, which has potential applications in a variety of gesture interaction scenarios not only in social robots.

Tumor-derived Cav-1 promotes pre-metastatic niche formation and lung metastasis in breast cancer.

Rationale: Breast cancer (BC), as one of the most frequently diagnosed cancer, has a poor prognosis due to the development of distant metastasis. Among the BC metastatic sites, lung is one of the most common sites. Caveolin-1 (Cav-1) is a functional membrane protein that plays a vital role in tumor metastasis. Although studies have revealed that Cav-1 levels were elevated in patients with advanced cancer, whether Cav-1 affects BC lung metastasis by influencing the formation of pre-metastatic niche (PMN) through exosomes has not been explored. Methods: Differential ultracentrifugation, transmission electron microscopy and nanoparticle tracking analysis were used to verify the presence of exosomes. Transwell assays were used to examine the biological effects of exosomes containing Cav-1. Both in vitro cell cultures and mammary tumor cell-induced mouse models were used to assess the lung metastasis. The regulatory mechanisms of PMN formation were revealed using western blot, flow cytometry, RT-qPCR, immunofluorescence assays, gene overexpression assays and RNA interference assays. Results: Exosomes have critical functions in transporting Cav-1 between primary BC and metastatic organ microenvironments. Cav-1 in BC-derived exosomes can act as a signaling molecule to mediate intercellular communication and regulate the PMN before lung metastasis by regulating the expression of PMN marker genes and inflammatory chemokines in lung epithelial cells, promoting the secretion of tenascin-C (TnC) in lung fibroblasts to cause extracellular matrix (ECM) deposition, and inhibiting the PTEN/CCL2/VEGF-A signaling pathway in lung macrophages to facilitate their M2-type polarization and angiogenesis. Conclusion: Our study investigated the mechanisms of lung PMN formation induced by Cav-1 in BC-derived exosomes. Our data may provide new directions for exploring the mechanisms and developing treatment strategies of BC lung metastasis.

A Graph-Attention-Based Method for Single-Resident Daily Activity Recognition in Smart Homes.

In ambient-assisted living facilitated by smart home systems, the recognition of daily human activities is of great importance. It aims to infer the household's daily activities from the triggered sensor observation sequences with varying time intervals among successive readouts. This paper introduces a novel deep learning framework based on embedding technology and graph attention networks, namely the time-oriented and location-oriented graph attention (TLGAT) networks. The embedding technology converts sensor observations into corresponding feature vectors. Afterward, TLGAT provides a sensor observation sequence as a fully connected graph to the model's temporal correlation as well as the sensor's location correlation among sensor observations and facilitates the feature representation of each sensor observation through receiving other sensor observations and weighting operations. The experiments were conducted on two public datasets, based on the diverse setups of sensor event sequence length. The experimental results revealed that the proposed method achieved favorable performance under diverse setups.

HOTAIR/miR-203/CAV1 Crosstalk Influences Proliferation, Migration, and Invasion in the Breast Cancer Cell.

In recent years, malignant breast cancer metastasis has caused a great increase in mortality. Research on the genetic and molecular mechanisms of malignant breast cancer has continued to deepen, and targeted therapy has become the general trend. Among them, competing endogenous RNA (ceRNA)-related molecules have received much attention. Homeobox transcript antisense RNA (HOTAIR) has been reported to function extensively as a ceRNA in breast cancer. Notably, miR-203 and Caveolin 1 (CAV1) have also been found to play a role in breast cancer. However, the relationship between the three remains unclear. In this study, we present a new mechanic through bioinformatics tool and basic experiments: the HOTAIR/miR-203/CAV1 axis, which complemented the role network of HOTAIR as a ceRNA, thus, it will provide a novel potential idea for breast cancer research and therapy.

Caveolin-1-deficient fibroblasts promote migration, invasion, and stemness via activating the TGF-ß/Smad signaling pathway in breast cancer cells.

Cancer-associated fibroblasts (CAFs) represent one of the main components in the tumor stroma and play a key role in breast cancer progression. Transforming growth factor-ß (TGF-ß) has been established to mediate breast cancer metastasis by regulating the epithelial-mesenchymal transition (EMT) and stemness of cancer cells. Caveolin-1 (CAV-1) is a scaffold protein of caveolae that is related to the proliferation and metabolism of cancer cells. It is now well demonstrated that CAV-1 deficiency in the tumor stroma is positively correlated with distant metastasis, but the mechanism remains unclear. Here, we explore whether CAV-1-deficient fibroblasts play an essential role in the EMT and stemness of breast cancer cells (BCCs) through TGF-ß signaling. We establish a specific small interfering RNA (siRNA) to inhibit CAV-1 expression in fibroblasts and coculture them with BCCs to investigate the effect of CAV­1-deficient fibroblasts and the tumor microenvironment on breast cancer progression. This study refreshingly points out that CAV-1 deficiency in fibroblasts enhances TGF-ß1 secretion and then activates the TGF-ß1/Smad signaling pathway of BCCs, thus promoting the metastasis and stemness of BCCs. Collectively, our findings indicate an unexpected role of CAV-1 deficiency in fibroblasts and the tumor microenvironment as a permissive factor, which is regulated by the TGF-ß1 signaling pathway in BCCs.

Small Extracellular Vesicles: Functions and Potential Clinical Applications as Cancer Biomarkers.

Cancer, as the second leading cause of death worldwide, is a major public health concern that imposes a heavy social and economic burden. Effective approaches for either diagnosis or therapy of most cancers are still lacking. Dynamic monitoring and personalized therapy are the main directions for cancer research. Cancer-derived extracellular vesicles (EVs) are potential disease biomarkers. Cancer EVs, including small EVs (sEVs), contain unique biomolecules (protein, nucleic acid, and lipids) at various stages of carcinogenesis. In this review, we discuss the biogenesis of sEVs, and their functions in cancer, revealing the potential applications of sEVs as cancer biomarkers.

An Incremental Learning Framework to Enhance Teaching by Demonstration Based on Multimodal Sensor Fusion.

Though a robot can reproduce the demonstration trajectory from a human demonstrator by teleoperation, there is a certain error between the reproduced trajectory and the desired trajectory. To minimize this error, we propose a multimodal incremental learning framework based on a teleoperation strategy that can enable the robot to reproduce the demonstration task accurately. The multimodal demonstration data are collected from two different kinds of sensors in the demonstration phase. Then, the Kalman filter (KF) and dynamic time warping (DTW) algorithms are used to preprocessing the data for the multiple sensor signals. The KF algorithm is mainly used to fuse sensor data of different modalities, and the DTW algorithm is used to align the data in the same timeline. The preprocessed demonstration data are further trained and learned by the incremental learning network and sent to a Baxter robot for reproducing the task demonstrated by the human. Comparative experiments have been performed to verify the effectiveness of the proposed framework.

Robotic Impedance Learning for Robot-Assisted Physical Training.

Impedance control has been widely used in robotic applications where a robot has physical interaction with its environment. However, how the impedance parameters are adapted according to the context of a task is still an open problem. In this paper, we focus on a physical training scenario, where the robot needs to adjust its impedance parameters according to the human user's performance so as to promote their learning. This is a challenging problem as humans' dynamic behaviors are difficult to model and subject to uncertainties. Considering that physical training usually involves a repetitive process, we develop impedance learning in physical training by using iterative learning control (ILC). Since the condition of the same iteration length in traditional ILC cannot be met due to human variance, we adopt a novel ILC to deal with varying iteration lengthes. By theoretical analysis and simulations, we show that the proposed method can effectively learn the robot's impedance in the application of robot-assisted physical training.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.