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Wearable devices with bimanual force feedback enable natural and cooperative manipulations within an unrestricted space. Weight and cost have a great influence on the potential applications of a haptic device. This paper presents a wearable robotic interface with bimanual force feedback that has considerably reduced weight and cost. To make the reaction force less perceivable than the interaction force, a waist-worn scheme is adopted. The interface mainly consists of a belt, a fastening tape, two serial robotic arms, and two electronics units and batteries. The robotic arms located on both sides of the belt are capable of 3-DoF position tracking and force feedback for each hand. The whole interface is lightweight (only 2.4 kg) and accessible. Furthermore, it is also easy to wear and the operator can wear it only by putting the belt on the waist and fastening the tape, reducing his/her dependency on additional assistance. The interface is optimized to obtain desirable force output and a dexterous workspace without singularity. To evaluate its performance in bimanual cooperative manipulations, an experiment in the virtual environment was conducted. The experimental results showed the subjects had more efficient and stable cooperative manipulations with bimanual force feedback than without force feedback.Wearable haptic systems can be easily integrated with the human body and represent an effective solution for a natural and unobtrusive stimulus delivery. These characteristics can open interesting perspectives for different applications, such as haptic guidance for human ergonomics enhancement, e.g. during human-robot collaborative tasks in industrial scenarios, where the usage of the visual communication channel can be problematic. In this work, we propose a wearable multi-cue system that can be worn at the arm level on both the two upper limbs, which conveys both squeezing stimuli (provided by an armband haptic device) and vibration, to provide corrective feedback for posture balancing along the user's frontal and sagittal plane, respectively. We evaluated the effectiveness of our system in delivering directional information to control the user's center of pressure position on a balancing board. We compared the here proposed haptic guidance with visual guidance cues. Results show no statistically significant differences in terms of success rate and time for task completion for the two conditions. Furthermore, participants underwent through a Subjective Quantitative Evaluation and a NASA-TLX test, evaluating the wearable haptic system as intuitive and effective.We routinely communicate distinct social and emotional sentiments through nuanced touch. For example, we might gently hold anothers arm to offer a sense of calm, yet intensively hold anothers arm to express excitement or anxiety. As this example indicates, distinct sentiments may be shaped by the subtlety in ones touch delivery. This work investigates how slight distinctions in skin-to-skin contact influence both the recognition of cued emotional messages (e.g., anger, sympathy) and the rating of emotional content (i.e., arousal, valence). By self-selecting preferred gestures (e.g., holding, stroking), touchers convey distinct messages by touching the receivers forearm. Skin-to-skin contact attributes (e.g., velocity, depth, area) are optically tracked in high resolution. Contact is then examined within gesture, between messages. The results indicate touchers subtly, but significantly, vary contact attributes of a gesture to communicate distinct messages, which are recognizable by receivers. This tuning also correlates with receivers arousal and valence. For instance, arousal increases with velocity for stroking, and depth for holding. Moreover, as shown here with human-to-human touch, valence is tied with velocity, which is the same trend as reported with brushes. The findings indicate that subtle nuance in skin-to-skin contact is important in conveying social messages and inducing emotions.Plant stomata phenotypic traits can provide a basis for enhancing crop tolerance in adversity. Manually counting the number of stomata and measuring the height and width of stomata obviously cannot satisfy the high-throughput data. How to detect and recognize plant stomata quickly and accurately is the prerequisite and key for studying the physiological characteristics of stomata. In this research, we consider stomata recognition as a multi-object detection problem, and propose an end-to-end framework for intelligent detection and recognition of plant stomata based on feature weights transfer learning and YOLOv4 network. It is easy to operate and greatly facilitates the analysis of stomata phenotypic traits in high-throughput plant epidermal cell images. For different cultivars, multi-scales, rich background features, high density, and small stomata object images, the proposed method can precisely locate multiple stomata in microscope images and automatically give phenotypic traits of stomata. Users can also adjust the corresponding parameters to maximize the accuracy and scalability of automatic stomata detection and recognition. Experimental results on actual data provided by the National Maize Improvement Center show that the proposed method is superior to the existing methods in high stomata automatic detection and recognition accuracy, low training cost, strong generalization ability.Effective estimation of brain network connectivity enables better unraveling of the extraordinary complexity interactions of brain regions and helps in auxiliary diagnosis of psychiatric disorders. Considering different modalities can provide comprehensive characterizations of brain connectivity, we propose the message-passing-based nonlinear network fusion (MP-NNF) algorithm to estimate multimodal brain network connectivity. In the proposed method, the initial functional and structural networks were computed from fMRI and DTI separately. Then, we update every unimodal network iteratively, making it more similar to the others in every iteration and finally converge to one unified network. The estimated brain connectivities integrate complementary information of from multiple modalities while preserving their original structure, by adding the strong connectivities present in unimodal brain networks and eliminating the weak connectivities. The effectiveness of the method was evaluated by applying the learned brain connectivity for the classification of major depressive disorder (MDD). Specifically, 82.18% classification accuracy was achieved even with the simple feature selection and classification pipeline, which significantly outperforms the competing methods. Exploration of brain connectivity contributed to MDD identification suggests that the proposed method not only improves the classification performance but also was sensitive to critical disease-related neuroimaging biomarkers.Protein-Protein Interactions (PPIs) are a crucial mechanism underpinning the function of the cell. So far, a wide range of machine-learning based methods have been proposed for predicting these relationships. Their success is heavily dependent on the construction of the underlying feature vectors, with most using a set of physico-chemical properties derived from the sequence. Few work directly with the sequence itself. In this paper, we explore the utility of sequence embeddings for predicting protein-protein interactions. We construct a protein pair feature vector by concatenating the embeddings of their constituent sequence. These feature vectors are then used as input to a binary classifier to make predictions. To learn sequence embeddings, we use two established methods, Seq2Vec and BioVec, and we also introduce a novel feature construction method called SuperVecNW. The embeddings generated through SuperVecNW capture some network information in addition to the contextual information present in the sequences. We test the efficacy of our proposed approach on human and yeast PPI datasets and on three well-known networks CD9, Ras-Raf-Mek-Erk-Elk-Srf pathway, and Wnt-related network. We demonstrate that low dimensional sequence embeddings provide better results than most alternative representations based on physico-chemical properties while offering a far simple approach to feature vector construction.An increasing number of patients are suffering from central nervous system (CNS) injury, including spinal cord injury. However, no suitable treatment is available for such patients as yet. https://www.selleckchem.com/products/rmc-4630.html Various platforms have been utilized to recapitulate CNS injuries. However, animal models and in vitro two-dimensional (2D)-based cell culture platforms have limitations, such as genetic heterogeneity and loss of the neural-circuit ultrastructure. To overcome these limitations, we developed a method for performing axotomy on an open-access three-dimensional (3D) neuron-culture platform. In this platform, the 3D alignment of axons in the brain tissue was recapitulated. For direct access to the cultured axons, the bottom of the 3D neuron-culture device was disassembled, enabling exposure of the neuron-laden Matrigel to the outside. The mechanical damage to the axons was recapitulated by puncturing the neuron-laden Matrigel using a pin. Thus, precise axotomy of three-dimensionally aligned axons could be performed. Furthermore, it was possible to fill the punctuated area by re-injecting Matrigel. Consequently, neurites regenerated into re-injected Matrigel. Moreover, it was confirmed that astrocytes can be co-cultured on this open-access platform without interfering with the axon alignment. The proposed open-access platform is expected to be useful for developing treatment techniques for CNS injuries.The mechanical properties of cells play important roles in regulating the physiological activities of cells and reflect the state of macro-organisms. Although many approaches are available for investigating the mechanical properties of cells, the fluidity of cytoplasm across cell boundaries makes characterizing the dynamics of mechanical properties of single cells exceedingly difficult. In this study, we present a single cell characterization method by modelling the dynamics of cellular mechanical properties measured with an atomic force microscope (AFM). The mechanical dynamics of a single cell system was described by a linear model with a mechanical stimulus as virtual input and mechanical property parameters as outputs. The dynamic mechanical properties of a single cell were characterized by the system matrix of the single cell system. The method was used to classify different types of cells, and the experimental results show that the proposed method outperformed conventional methods by achieving an average classification accuracy of over 90%. The developed method can be used to classify different cancer types according to the mechanical properties of tumour cells, which is of great significance for clinically assisted pathological diagnosis.Retinal prostheses aim to improve visual perception in patients blinded by photoreceptor degeneration. However, shape and letter perception with these devices is currently limited due to low spatial resolution. Previous research has shown the retinal ganglion cell (RGC) spatial activity and phosphene shapes can vary due to the complexity of retina structure and electrode-retina interactions. Visual percepts elicited by single electrodes differ in size and shapes for different electrodes within the same subject, resulting in interference between phosphenes and an unclear image. Prior work has shown that better patient outcomes correlate with spatially separate phosphenes. In this study we use calcium imaging, in vitro retina, neural networks (NN), and an optimization algorithm to demonstrate a method to iteratively search for optimal stimulation parameters that create focal RGC activation. Our findings indicate that we can converge to stimulation parameters that result in focal RGC activation by sampling less than 1/3 of the parameter space.