Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands

Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system’s complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs.     

An Optimal Free Energy Dissipation Strategy of the MinCDE Oscillator in Regulating Symmetric Bacterial Cell Division

Sustained molecular oscillations are ubiquitous in biology. The obtained oscillatory patterns provide vital functions as timekeepers, pacemakers and spacemarkers. Models based on control theory have been introduced to explain how specific oscillatory behaviors stem from protein interaction feedbacks, whereas the energy dissipation through the oscillating processes and its role in the regulatory function remain unexplored. Here we developed a general framework to assess an oscillator’s regulation performance at different dissipation levels. Using the Escherichia coli MinCDE oscillator as a model system, we showed that a sufficient amount of energy dissipation is needed to switch on the oscillation, which is tightly coupled to the system’s regulatory performance. Once the dissipation level is beyond this threshold, unlike stationary regulators’ monotonic performance-to-cost relation, excess dissipation at certain steps in the oscillating process damages the oscillator’s regulatory performance. We further discovered that the chemical free energy from ATP hydrolysis has to be strategically assigned to the MinE-aided MinD release and the MinD immobilization steps for optimal performance, and a higher energy budget improves the robustness of the oscillator. These results unfold a novel mode by which living systems trade energy for regulatory function.     

Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter

The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system’s efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.     

What Do Contrast Threshold Equivalent Noise Studies Actually Measure? Noise vs. Nonlinearity in Different Masking Paradigms

The internal noise present in a linear system can be quantified by the equivalent noise method. By measuring the effect that applying external noise to the system’s input has on its output one can estimate the variance of this internal noise. By applying this simple “linear amplifier” model to the human visual system, one can entirely explain an observer’s detection performance by a combination of the internal noise variance and their efficiency relative to an ideal observer. Studies using this method rely on two crucial factors: firstly that the external noise in their stimuli behaves like the visual system’s internal noise in the dimension of interest, and secondly that the assumptions underlying their model are correct (e.g. linearity). Here we explore the effects of these two factors while applying the equivalent noise method to investigate the contrast sensitivity function (CSF). We compare the results at 0.5 and 6 c/deg from the equivalent noise method against those we would expect based on pedestal masking data collected from the same observers. We find that the loss of sensitivity with increasing spatial frequency results from changes in the saturation constant of the gain control nonlinearity, and that this only masquerades as a change in internal noise under the equivalent noise method. Part of the effect we find can be attributed to the optical transfer function of the eye. The remainder can be explained by either changes in effective input gain, divisive suppression, or a combination of the two. Given these effects the efficiency of our observers approaches the ideal level. We show the importance of considering these factors in equivalent noise studies.     

Stochastic sampling of the RNA structural alignment space

A novel method is presented for predicting the common secondary structures and alignment of two homologous RNA sequences by sampling the ‘structural alignment’ space, i.e. the joint space of their alignments and common secondary structures. The structural alignment space is sampled according to a pseudo-Boltzmann distribution based on a pseudo-free energy change that combines base pairing probabilities from a thermodynamic model and alignment probabilities from a hidden Markov model. By virtue of the implicit comparative analysis between the two sequences, the method offers an improvement over single sequence sampling of the Boltzmann ensemble. A cluster analysis shows that the samples obtained from joint sampling of the structural alignment space cluster more closely than samples generated by the single sequence method. On average, the representative (centroid) structure and alignment of the most populated cluster in the sample of structures and alignments generated by joint sampling are more accurate than single sequence sampling and alignment based on sequence alone, respectively. The ‘best’ centroid structure that is closest to the known structure among all the centroids is, on average, more accurate than structure predictions of other methods. Additionally, cluster analysis identifies, on average, a few clusters, whose centroids can be presented as alternative candidates. The source code for the proposed method can be downloaded at http://rna.urmc.rochester.edu.     

New Approaches for Calculating Moran’s Index of Spatial Autocorrelation

Spatial autocorrelation plays an important role in geographical analysis; however, there is still room for improvement of this method. The formula for Moran’s index is complicated, and several basic problems remain to be solved. Therefore, I will reconstruct its mathematical framework using mathematical derivation based on linear algebra and present four simple approaches to calculating Moran’s index. Moran’s scatterplot will be ameliorated, and new test methods will be proposed. The relationship between the global Moran’s index and Geary’s coefficient will be discussed from two different vantage points: spatial population and spatial sample. The sphere of applications for both Moran’s index and Geary’s coefficient will be clarified and defined. One of theoretical findings is that Moran’s index is a characteristic parameter of spatial weight matrices, so the selection of weight functions is very significant for autocorrelation analysis of geographical systems. A case study of 29 Chinese cities in 2000 will be employed to validate the innovatory models and methods. This work is a methodological study, which will simplify the process of autocorrelation analysis. The results of this study will lay the foundation for the scaling analysis of spatial autocorrelation.     

Focus: The Aging Brain: Cross-education of strength and skill: an old idea with applications in the aging nervous system

Edward Wheeler Scripture’s 1894 work out of the Yale Psychological Laboratory has been influential in identifying the nervous system’s contribution to the bilateral improvements that are seen with unilateral strength and skill training. Scripture coined the term “cross-education” to describe this improvement in the untrained contralateral limb. While physiological changes accompany aging that may negatively affect the performance of physical tasks, far too much credit has been given to the natural aging process rather than the effects of inactivity. Emerging evidence indicates strength or skill training interventions induce significant neuroplasticity in an aging population. The model of unilateral training provides a unique approach in which to elicit such plasticity. This brief review highlights the innate ability of the nervous system to adapt to unilateral strength and skill training interventions, regardless of age, and provides a novel perspective on the robust plastic ability of the aging nervous system.     

Unexpectedly broad target recognition of the CRISPR-mediated virus defence system in the archaeon Sulfolobus solfataricus

The hyperthermophilic archaeon Sulfolobus solfataricus carries an extensive array of clustered regularly interspaced short palindromic repeats (CRISPR) systems able to mediate DNA degradation of invading genetic elements when complementarity to the small CRISPR-derived (cr)RNAs is given. Studying virus defence in vivo with recombinant viral variants, we demonstrate here that an unexpectedly high number of mutations are tolerated between the CRISPR-derived guide RNAs (crRNAs) and their target sequences (protospacer). Up to 15 mismatches in the crRNA still led to ∼50% of DNA degradation, when these mutations were outside the ‘seed’ region. More than 15 mutations were necessary to fully abolished interference. Different from other CRISPR systems investigated in vivo, mutations outside the protospacer region indicated no need for a protospacer adjacent motif sequence to confer DNA interference. However, complementarity of only 3 nucleotides between the repeat-derived 5′ handle of the crRNA and nucleotides adjacent to the protospacer enabled self-recognition, i.e. protection of the host locus. Our findings show commonalities and differences among the various CRISPR-mediated defence systems and suggest that they should not merely be perceived as a ‘first-barrier-defence system’ but may be considered to have a broader mechanism that allows host cells to cope with viruses keeping them at reduced levels.     

Sensitivity and Robustness in Covalent Modification Cycles with a Bifunctional Converter Enzyme

Regulation by covalent modification is a common mechanism to transmit signals in biological systems. The modifying reactions are catalyzed either by two distinct converter enzymes or by a single bifunctional enzyme (which may employ either one or two catalytic sites for its opposing activities). The reason for this diversification is unclear, but contemporary theoretical models predict that systems with distinct converter enzymes can exhibit enhanced sensitivity to input signals whereas bifunctional enzymes with two catalytic sites are believed to generate robustness against variations in system’s components. However, experiments indicate that bifunctional enzymes can also exhibit enhanced sensitivity due to the zero-order effect, raising the question whether both phenomena could be understood within a common mechanistic model. Here, I argue that this is, indeed, the case. Specifically, I show that bifunctional enzymes with two catalytic sites can exhibit both ultrasensitivity and concentration robustness, depending on the kinetic operating regime of the enzyme’s opposing activities. The model predictions are discussed in the context of experimental observations of ultrasensitivity and concentration robustness in the uridylylation cycle of the PII protein, and in the phosphorylation cycle of the isocitrate dehydrogenase, respectively.     

Microscale Vortex-assisted Electroporator for Sequential Molecular Delivery

Electroporation has received increasing attention in the past years, because it is a very powerful technique for physically introducing non-permeant exogenous molecular probes into cells. This work reports a microfluidic electroporation platform capable of performing multiple molecule delivery to mammalian cells with precise and molecular-dependent parameter control. The system’s ability to isolate cells with uniform size distribution allows for less variation in electroporation efficiency per given electric field strength; hence enhanced sample viability. Moreover, its process visualization feature allows for observation of the fluorescent molecular uptake process in real-time, which permits prompt molecular delivery parameter adjustments in situ for efficiency enhancement. To show the vast capabilities of the reported platform, macromolecules with different sizes and electrical charges (e.g., Dextran with MW of 3,000 and 70,000 Da) were delivered to metastatic breast cancer cells with high delivery efficiencies (>70%) for all tested molecules. The developed platform has proven its potential for use in the expansion of research fields where on-chip electroporation techniques can be beneficial.     

Feasibility of an Ingestible Sensor-Based System for Monitoring Adherence to Tuberculosis Therapy

Poor adherence to tuberculosis (TB) treatment hinders the individual’s recovery and threatens public health. Currently, directly observed therapy (DOT) is the standard of care; however, high sustaining costs limit its availability, creating a need for more practical adherence confirmation methods. Techniques such as video monitoring and devices to time-register the opening of pill bottles are unable to confirm actual medication ingestions. A novel approach developed by Proteus Digital Health, Inc. consists of an ingestible sensor and an on-body wearable sensor; together, they electronically confirm unique ingestions and record the date/time of the ingestion. A feasibility study using an early prototype was conducted in active TB patients to determine the system’s accuracy and safety in confirming co-ingestion of TB medications with sensors. Thirty patients completed 10 DOT visits and 1,080 co-ingestion events; the system showed 95^.0% (95% CI 93^.5–96^.2%) positive detection accuracy, defined as the number of detected sensors divided by the number of transmission capable sensors administered. The specificity was 99^.7% [95% CI 99^.2–99^.9%] based on three false signals recorded by receivers. The system’s identification accuracy, defined as the number of correctly identified ingestible sensors divided by the number of sensors detected, was 100%. Of 11 adverse events, four were deemed related or possibly related to the device; three mild skin rashes and one complaint of nausea. The system’s positive detection accuracy was not affected by the subjects’ Body Mass Index (p = 0^.7309). Study results suggest the system is capable of correctly identifying ingestible sensors with high accuracy, poses a low risk to users, and may have high patient acceptance. The system has the potential to confirm medication specific treatment compliance on a dose-by-dose basis. When coupled with mobile technology, the system could allow wirelessly observed therapy (WOT) for monitoring TB treatment as a replacement for DOT.     

Site-Directed Mutagenesis to Improve Sensitivity of a Synthetic Two-Component Signaling System

Two-component signaling (2CS) systems enable bacterial cells to respond to changes in their local environment, often using a membrane-bound sensor protein and a cytoplasmic responder protein to regulate gene expression. Previous work has shown that Escherichia coli’s natural EnvZ/OmpR 2CS could be modified to construct a light-sensing bacterial photography system. The resulting bacterial photographs, or “coliroids,” rely on a phosphotransfer reaction between Cph8, a synthetic version of EnvZ that senses red light, and OmpR. Gene expression changes can be visualized through upregulation of a LacZ reporter gene by phosphorylated OmpR. Unfortunately, basal LacZ expression leads to a detectable reporter signal even when cells are grown in the light, diminishing the contrast of the coliroids. We performed site-directed mutagenesis near the phosphotransfer site of Cph8 to isolate mutants with potentially improved image contrast. Five mutants were examined, but only one of the mutants, T541S, increased the ratio of dark/light gene expression, as measured by β-galactosidase activity. The ratio changed from 2.57 fold in the starting strain to 5.59 in the T541S mutant. The ratio decreased in the four other mutant strains we examined. The phenotype observed in the T541S mutant strain may arise because the serine sidechain is chemically similar but physically smaller than the threonine sidechain. This may minimally change the protein’s local structure, but may be less sterically constrained when compared to threonine, resulting in a higher probability of a phosphotransfer event. Our initial success pairing synthetic biology and site-directed mutagenesis to optimize the bacterial photography system’s performance encourages us to imagine further improvements to the performance of this and other synthetic systems, especially those based on 2CS signaling.     

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein’s function in the cell. Understanding a protein’s secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.     

Multistability in Large Scale Models of Brain Activity

Noise driven exploration of a brain network’s dynamic repertoire has been hypothesized to be causally involved in cognitive function, aging and neurodegeneration. The dynamic repertoire crucially depends on the network’s capacity to store patterns, as well as their stability. Here we systematically explore the capacity of networks derived from human connectomes to store attractor states, as well as various network mechanisms to control the brain’s dynamic repertoire. Using a deterministic graded response Hopfield model with connectome-based interactions, we reconstruct the system’s attractor space through a uniform sampling of the initial conditions. Large fixed-point attractor sets are obtained in the low temperature condition, with a bigger number of attractors than ever reported so far. Different variants of the initial model, including (i) a uniform activation threshold or (ii) a global negative feedback, produce a similarly robust multistability in a limited parameter range. A numerical analysis of the distribution of the attractors identifies spatially-segregated components, with a centro-medial core and several well-delineated regional patches. Those different modes share similarity with the fMRI independent components observed in the “resting state” condition. We demonstrate non-stationary behavior in noise-driven generalizations of the models, with different meta-stable attractors visited along the same time course. Only the model with a global dynamic density control is found to display robust and long-lasting non-stationarity with no tendency toward either overactivity or extinction. The best fit with empirical signals is observed at the edge of multistability, a parameter region that also corresponds to the highest entropy of the attractors.     

KS-Detect – Validation of Solar Thermal PCR for the Diagnosis of Kaposi’s Sarcoma Using Pseudo-Biopsy Samples

Resource-limited settings present unique engineering challenges for medical diagnostics. Diagnosis is often needed for those unable to reach central healthcare systems, making portability and independence from traditional energy infrastructure essential device parameters. In 2014, our group presented a microfluidic device that performed a solar-powered variant of the polymerase chain reaction, which we called solar thermal PCR. In this work, we expand on our previous effort by presenting an integrated, portable, solar thermal PCR system targeted towards the diagnosis of Kaposi’s sarcoma. We call this system KS-Detect, and we now report the system’s performance as a diagnostic tool using pseudo-biopsy samples made from varying concentrations of human lymphoma cell lines positive for the KS herpesvirus (KSHV). KS-Detect achieved 83% sensitivity and 70% specificity at high (≥10%) KSHV+ cell concentrations when diagnosing pseudo-biopsy samples by smartphone image. Using histology, we confirm that our prepared pseudo-biopsies contain similar KSHV+ cell concentrations as human biopsies positive for KS. Through our testing of samples derived from human cell lines, we validate KS-Detect as a viable, portable KS diagnostic tool, and we identify critical engineering considerations for future solar-thermal PCR devices.     

Information-Driven Active Audio-Visual Source Localization

We present a system for sensorimotor audio-visual source localization on a mobile robot. We utilize a particle filter for the combination of audio-visual information and for the temporal integration of consecutive measurements. Although the system only measures the current direction of the source, the position of the source can be estimated because the robot is able to move and can therefore obtain measurements from different directions. These actions by the robot successively reduce uncertainty about the source’s position. An information gain mechanism is used for selecting the most informative actions in order to minimize the number of actions required to achieve accurate and precise position estimates in azimuth and distance. We show that this mechanism is an efficient solution to the action selection problem for source localization, and that it is able to produce precise position estimates despite simplified unisensory preprocessing. Because of the robot’s mobility, this approach is suitable for use in complex and cluttered environments. We present qualitative and quantitative results of the system’s performance and discuss possible areas of application.     

Functional Aspects of the EGF-Induced MAP Kinase Cascade: A Complex Self-Organizing System Approach

The EGF-induced MAP kinase cascade is one of the most important and best characterized networks in intracellular signalling. It has a vital role in the development and maturation of living organisms. However, when deregulated, it is involved in the onset of a number of diseases. Based on a computational model describing a “surface” and an “internalized” parallel route, we use systems biology techniques to characterize aspects of the network’s functional organization. We examine the re-organization of protein groups from low to high external stimulation, define functional groups of proteins within the network, determine the parameter best encoding for input intensity and predict the effect of protein removal to the system’s output response. Extensive functional re-organization of proteins is observed in the lower end of stimulus concentrations. As we move to higher concentrations the variability is less pronounced. 6 functional groups have emerged from a consensus clustering approach, reflecting different dynamical aspects of the network. Mutual information investigation revealed that the maximum activation rate of the two output proteins best encodes for stimulus intensity. Removal of each protein of the network resulted in a range of graded effects, from complete silencing to intense activation. Our results provide a new “vista” of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems. Functional grouping of the proteins reveals an organizational scheme contrasting the current understanding of modular topology. The six identified groups may provide the means to experimentally follow the dynamics of this complex network. Also, the vulnerability analysis approach may be used for the development of novel therapeutic targets in the context of personalized medicine.     

The estimation of statistical parameters for local alignment score distributions

The distribution of optimal local alignment scores of random sequences plays a vital role in evaluating the statistical significance of sequence alignments. These scores can be well described by an extreme-value distribution. The distribution’s parameters depend upon the scoring system employed and the random letter frequencies; in general they cannot be derived analytically, but must be estimated by curve fitting. For obtaining accurate parameter estimates, a form of the recently described ‘island’ method has several advantages. We describe this method in detail, and use it to investigate the functional dependence of these parameters on finite-length edge effects.     

Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch

Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element (a smartphone), this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch (both provided with an embedded accelerometer and a gyroscope). In the proposed architecture, a specific application in each component permanently tracks and analyses the patient’s movements. Diverse fall detection algorithms (commonly employed in the literature) were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices (which can interact via Bluetooth communication). The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects (i.e., the typology of the falls and non-fall movements). The proposed system was compared with the cases where only one device (the smartphone or the smartwatch) is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system’s capability to avoid false alarms or ‘false positives’ (those conventional movements misidentified as falls) while maintaining the effectiveness of the detection decisions (that is to say, without increasing the ratio of ‘false negatives’ or actual falls that remain undetected).