In the framework of rehabilitation robotics, a major role is played by the human–machine interface (HMI) used to gather the patient’s intent from biological signals, and convert them into control signals for the robotic artifact. Surprisingly, decades of research have not yet declared what the optimal HMI is in this context; in particular, the traditional approach based upon surface electromyography (sEMG) still yields unreliable results due to the inherent variability of the signal. To overcome this problem, the scientific community has recently been advocating the discovery, analysis, and usage of novel HMIs to supersede or augment sEMG; a comparative analysis of such HMIs is therefore a very desirable investigation. In this paper, we compare three such HMIs employed in the detection of finger forces, namely sEMG, ultrasound imaging, and pressure sensing. The comparison is performed along four main lines: the accuracy in the prediction, the stability over time, the wearability, and the cost. A psychophysical experiment involving ten intact subjects engaged in a simple finger-flexion task was set up. Our results show that, at least in this experiment, pressure sensing and sEMG yield comparably good prediction accuracies as opposed to ultrasound imaging; and that pressure sensing enjoys a much better stability than sEMG. Given that pressure sensors are as wearable as sEMG electrodes but way cheaper, we claim that this HMI could represent a valid alternative/augmentation to sEMG to control a multi-fingered hand prosthesis.

In rehabilitation robotics it is highly desirable to find novel human-machine interfaces for the disabled, in particular to substitute or augment surface electromyography (sEMG), trying to keep at the same time its easiness of use, precision and non-invasiveness. In this paper we design and demonstrate one such device, based upon Force-Sensing Resistors (FSRs). An array of 10 FSRs was wrapped around the proximal section of the forearm of ten intact subjects engaged in pressing on an accurate force sensor with their fingers (this includes the rotation of the thumb). The FSRs would detect the forearm surface deformations induced by muscle activity; the signals provided by the FSRs were then matched to the recorded forces. The experimental results show that finger forces can be predicted using this device with the same accuracy obtained in literature using sEMG. The device, even as an academic prototype, weighs about 65 grams and costs around 50 EUR. Thus, it is remarkably light and cheap in comparison to standard sEMG electrode arrays.

Single-cell transcriptomic data has the potential to radically redefine our view of cell-type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma subtypes with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.

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Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i.e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects. In this study, we present new results that identify specific parts of resting state and task-specific connectomes that are responsible for the unique signatures. We show that a very small part of the connectome can be used to derive features for discriminating between individuals. A network of these features is shown to achieve excellent training and test accuracy in matching imaging datasets. We show that these features are statistically significant, robust to perturbations, invariant across populations, and are localized to a small number of structural regions of the brain. Furthermore, we show that for task-specific connectomes, the regions identified by our method are consistent with their known functional characterization. We present a new matrix sampling technique to derive computationally efficient and accurate methods for identifying the discriminating sub-connectome and support all of our claims using state-of-the-art statistical tests and computational techniques.

Graph databases have been the subject of significant research and development in the database, data analytics, and applications' communities. Problems such as modularity, centrality, alignment, and clustering have been formalized and solved in various application contexts. In this paper, we focus on databases for applications in which graphs have a spatial basis, which we refer to as rigid graphs. Nodes in such graphs have preferred positions relative to their graph neighbors. Examples of such graphs include abstractions of large biomolecules (e.g., in drug databases), where edges corresponding to chemical bonds have preferred lengths, functional connectomes of the human brain (e.g., the HCP database [13]), where edges corresponding to co-firing regions of the brain have preferred anatomical distances, and mobile device/ sensor communication logs, where edges corresponding to point-to-point communications across devices have distance constraints. When analyzing such networks it is important to consider edge lengths; e.g., when identifying conserved patterns through graph alignment, it is important for conserved edges to have correlated lengths, in addition to topological similarity. Similar considerations exist for clustering (densely connected regions of short edges) and centrality (critical edges with large weights). Problem Formulation Problem definitionWe define the rigid graph alignment problem by first reviewing existing graph and structure alignment formulations, and use these to motivate our new problem.

Advances in imaging technologies, combined with inexpensive storage, have led to an explosion in the volume of publicly available neuroimaging datasets. Effective analyses of these images hold the potential for uncovering mechanisms that govern functioning of the human brain, and understanding various neurological diseases and disorders. The potential significance of these studies notwithstanding, a growing concern relates to the protection of privacy and confidentiality of subjects who participate in these studies. In this paper, we present a de-anonymization attack rooted in the innate uniqueness of the structure and function of the human brain. We show that the attack reveals not only the identity of an individual, but also the task they are performing, and their efficacy in performing the tasks. Our attack relies on novel matrix analyses techniques that are used to extract discriminating features in neuroimages. These features correspond to individual-specific signatures that can be matched across datasets to yield highly accurate identification. We present data preprocessing, signature extraction, and matching techniques that are computationally inexpensive, and can scale to large datasets. We discuss implications of the attack and challenges associated with defending against such attacks. BackgroundStudies of high-resolution images of the brain provide the basis for our understanding of the essential processes that underlie the functioning of the brain, help characterize behaviour and anatomy, and their dys-regulation due to disease, neurological disorders, and aging. Images of the brain, neuroimages, serve as phenotypes (observables) of structure and function. Various modalities of imaging rely on different biophysical processes and properties of the brain tissue. Consequently, these modalities highlight different aspects of the brain -broadly speaking these images reveal anatomical structure or behavioral function. Anatomical images are high resolution (< 1 mm side) 3-D images that capture subtle structural details. Structural Magnetic Resonance Images (Structural MRI) can reveal clear boundaries between gray and white matter. Diffusion Tensor Imaging (DTI) and Diffusion Weighted Imaging (DWI) help visualize structural neuronal pathways. The physical manifestation of these features -volumes of gray and white matter and direction of neuronal pathways, are often dys-regulated in subjects with neurological disorders such as Attention Deficiency Disorder (ADD) and Attention Deficiency Hyperactive Disorder (ADHD), or neurodenerative diseases such as Alzheimer's and Parkinson's.Functional images are typically lower resolution 4-dimensional images (three spatial dimensions and one temporal dimension). Commonly used modalities for functional imaging include functional MRIs (fMRI), Electron Encephalography (EEG), and Magnetic Encephalography (MEG). Functional images capture dynamic behaviour of the brain, both in absence of stimulus (called resting-state) and in presence of stimulus (called task-specific). They re...

Single-cell transcriptomic data has the potential to radically redefine our view of cell type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma sub-types with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.