Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Control of Posture During Small Magnitude Perturbations: Effects of Aging and Localized Muscle Fatigue

This study investigated the effects of aging and localized muscle fatigue on the neural control of upright stance during small postural perturbations. Sixteen young (aged 18-24 years) and 16 older (aged 55-74 years) participants were exposed to small magnitude, anteriorly-directed postural perturbations before and after fatiguing exercises (lumbar extensors and ankle plantar flexors). A single degree of freedom model of the human body was used to simulate recovery kinematics following the perturbations. Central to the model was a simulated neural controller that multiplied time-delayed kinematics by invariant feedback gains. Feedback gains and time delay were optimized for each participant based on measured kinematics, and a novel delay margin analysis was performed to assess system robustness. A 10.9% longer effective time delay (p = 0.010) was found among the older group, who also showed a greater reliance upon velocity feedback information (31.1% higher differential gain, p = 0.001) to control upright stance. Based on delay margins, older participants adopted a more robust control scheme to accommodate the small perturbations, potentially compensating for longer time delays or degraded sensory feedback. No fatigue-induced changes in neural controller gains, time delay, or delay margin were found in either age group, indicating that integration of this feedback information was not altered by muscle fatigue. The sensitivity of this approach to changes with fatigue may have been limited by model simplifications.
Novel Dry Polymer Foam Electrodes for Long-Term EEG Measurement

A novel dry foam-based electrode for long-term EEG measurement was proposed in this study. In general, the conventional wet electrodes are most frequently used for EEG measurement. However, they require skin preparation and conduction gels to reduce the skin-electrode contact impedance. The aforementioned procedures when wet electrodes were used usually make trouble to users easily. In order to overcome the aforesaid issues, a novel dry foam electrode, fabricated by electrically conductive polymer foam covered by a conductive fabric, was proposed. By using conductive fabric, which provides partly polarizable electric characteristic, our dry foam electrode exhibits both polarization and conductivity, and can be used to measure biopotentials without skin preparation and conduction gel. In addition, the foam substrate of our dry electrode allows a high geometric conformity between the electrode and irregular scalp surface to maintain low skin-electrode interface impedance, even under motion. The experimental results presented that the dry foam electrode performs better for long-term EEG measurement, and is practicable for daily life applications.
Real-Time Multimodal Retinal Image Registration for a Computer-Assisted Laser Photocoagulation System

An algorithm for the real-time registration of a retinal video sequence captured with a scanning digital ophthal moscope (SDO) to a retinal composite image is presented. This method is designed for a computer-assisted retinal laser photocoagulation system to compensate for retinal motion and hence enhance the accuracy, speed, and patient safety of retinal laser treatments. The procedure combines intensity and feature-based registration techniques. For the registration of an individual frame, the translational frame-to-frame motion between preceding and current frame is detected by normalized cross correlation. Next, vessel points on the current video frame are identified and an initial transformation estimate is constructed from the calculated translation vector and the quadratic registration matrix of the previous frame. The vessel points are then iteratively matched to the segmented vessel centerline of the composite image to refine the initial transformation and register the video frame to the composite image. Criteria for image quality and algorithm convergence are introduced, which assess the exclusion of single frames from the registration process and enable a loss of tracking signal if necessary. The algorithm was successfully applied to ten different video sequences recorded from patients. It revealed an average accuracy of 2.47 ±2.0 pixels ( ~ 23.2 ±18.8 μm) for 2764 evaluated video frames and demonstrated that it meets the clinical requirements.
A Multiscale Framework Based on the Physiome Markup Languages for Exploring the Initiation of Osteoarthritis at the Bone–Cartilage Interface

The initiation of osteoarthritis (OA) has been linked to the onset and progression of pathologic mechanisms at the cartilage-bone interface. Most importantly, this degenerative disease involves cross-talk between the cartilage and subchondral bone environments, so an informative model should contain the complete complex. In order to evaluate this process, we have developed a multiscale model using the open-source ontologies developed for the Physiome Project with cartilage and bone descriptions at the cellular, micro, and macro levels. In this way, we can effectively model the influence of whole body loadings at the macro level and the influence of bone organization and architecture at the micro level, and have cell level processes that determine bone and cartilage remodeling. Cell information is then passed up the spatial scales to modify micro architecture and provide a macro spatial characterization of cartilage inflammation. We evaluate the framework by linking a common knee injury (anterior cruciate ligament deficiency) to proinflammatory mediators as a possible pathway to initiate OA. This framework provides a “virtual bone-cartilage” tool for evaluating hypotheses, treatment effects, and disease onset to inform and strengthen clinical studies.
Conscientious objection and nurses: Results of an interpretive phenomenological study

Background: While conscientious objection is a well-known phenomenon in normative and bioethical literature, there is a lack of evidence to support an understanding of what it is like for nurses to make a conscientious objection in clinical practice including the meaning this holds for them and the nursing profession. Research question: The question guiding this research was: what is the lived experience of conscientious objection for Registered Nurses in Ontario? Research design: Interpretive phenomenological methodology was used to gain an in-depth understanding of what it means to be a nurse making a conscientious objection. Purposive sampling with in-depth interview methods was used to collect and then analyze data through an iterative process. Participants and research context: Eight nurse participants were interviewed from across practice settings in Ontario, Canada. Each participant was interviewed twice over 9 months. Ethical considerations: This study was conducted in accordance with Health Science Research Ethics Board approval and all participants gave consent. Findings: Six themes emerged from data analysis: encountering the problem, knowing oneself, taking a stand, alone and uncertain, caring for others, and perceptions of support. Discussion: This study offers an initial understanding of what it is like to be a nurse making a conscientious objection in clinical practice. Implications for nursing practice, education, policy, and further research are discussed. Conclusion: Addressing ethical issues in nursing practice is complex. The need for education across nursing, healthcare disciplines and socio-political sectors is essential to respond to nurses’ ethical concerns giving rise to objections. Conscience emerged as an informant to nurses’ conscientious objections. The need for morally inclusive environments and addressing challenging ethical questions as well as the concept of conscience are relevant to advancing nursing ethics and ethical nursing practice.
Modeling the Human Knee for Assistive Technologies

In this paper, we use motion capture technology together with an EMG-driven musculoskeletal model of the knee joint to predict muscle behavior during human dynamic movements. We propose a muscle model based on infinitely stiff tendons and show this allows speeding up 250 times the computation of muscle force and the resulting joint moment calculation with no loss of accuracy with respect to the previously developed elastic-tendon model. We then integrate our previously developed method for the estimation of 3-D musculotendon kinematics in the proposed EMG-driven model. This new code enabled the creation of a standalone EMG-driven model that was implemented and run on an embedded system for applications in assistive technologies such as myoelectrically controlled prostheses and orthoses.
Differentiation between Alzheimer's disease and multi‐infarct dementia: Spect vs MR imaging

Single photon emission computed tomography (SPECT) and magnetic resonance (MR) imaging data were obtained in 11 patients with dementia of the Alzheimer type (DAT), 11 with multi‐infarct dementia (MID) and nine elderly, healthy controls. All patients with dementia tolerated a 15‐minute SPECT scan while six patients (27%) did not tolerate the MR examination due to the long acquisition times and/or claustrophobia. Blind qualitative rating of SPECT scans differentiated correctly between dementia and controls in 90% of cases (p < 0.0001; positive predicted value of 91%) and between clinically defined DAT and MID in 77% of cases (p < 0.01; positive predicted value of 80%). It correctly identified clinical group membership (control, DAT or MID) in 71% of cases (p <0.001; predicted positive value of 94%) but failed to differentiate between DAT and MID (positive predicted value of 50%). Overall, MR imaging correctly identified clinical group membership in 61% of cases (p < 0.01). Semiquantitative SPECT data demonstrated reduced regional cerebral blood flow (rCBF) in all regions of interest (ROI) except right frontal and right anterior parietal in both DAT and MID compared with controls (p < 0.05). There was no statistically significant difference between DAT and MID subjects in rCBF in any ROI. Several correlations between rCBF and cognitive variables were found in the MID but not the DAT group. Though the sample in this study is small, our findings suggest that Tc‐99m HMPAO SPECT is a useful and practical tool in the assessment of patients with dementia. It enables the differential diagnosis of dementia and by using semiquantitative data it helps to establish the severity of disease.
Reducing the power consumption of FPGAs through retiming

High power dissipation is one of the major disadvantages of FPGAs. A main part of the power consumed is caused by glitches. This paper analyzes the effect of retiming to reduce the power dissipation of a Xilinx Virtex-II FPGA. The authors introduce a method to insert staging registers into large designs, that are constructed from a high abstraction level language algorithmic description. Results obtained by measurements suggest a high potential for power savings through retiming.
Validação de equações antropométricas que estimam a densidade corporal em atletas profissionais de futebol

The objective of this study was to validate equations which estimate the body density in professional soccer players. Twenty-five soccer players were evaluated, aged 22.7 ± 4.4 years, body mass 73.9 ± 6.6 kg, and height 177.8 ± 5.5 cm, who played in the state championship of the Federacao Gaucha de Futebol in 2004. The validity of 11 anthropometric equations were analyzed by the process: Pearson correlation (r), dependent t test, constant error (EC), total error (ET) and estimated standard error (EPE), using hydrostatic weight as the "gold standard. " In the current study of 11 equations, only the equations proposed by Jackson and Pollock (1978) are valid for estimating the body density of professional soccer players, since the other equations analyzed in this study present considerable errors in their estimation.
Grey-box techniques for the identification of a controlled gene expression model

The aim of this paper is to propose a computationally efficient technique for the identification of stochastic biochemical networks, involving only zero and first order reactions, from distribution measurements of the cell population. The moments of the species in such networks evolve according to an affine system, hence the use of grey-box identification methods is suggested. The performance of existing methods and of a new method, based on the transfer function computation, is compared using as benchmark a standard gene expression model. The developed discussion is of interest for the general grey-box identification problem.
Measuring Time-Varying Information Flow in Scalp EEG Signals: Orthogonalized Partial Directed Coherence

This study aimed to develop a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded electroencephalographic (EEG) signals in a way that is less affected by volume conduction and amplitude scaling. We modified the time-varying generalized partial directed coherence (tv-gPDC) method, by orthogonalization of the strictly causal multivariate autoregressive model coefficients, to minimize the effect of mutual sources. The novel measure, generalized orthogonalized PDC (gOPDC), was tested first using two simulated models with feature dimensions relevant to EEG activities. We then used the method for assessing event-related directional information flow from flash-evoked responses in neonatal EEG. For testing statistical significance of the findings, we followed a thresholding procedure driven by baseline periods in the same EEG activity. The results suggest that the gOPDC method 1) is able to remove common components akin to volume conduction effect in the scalp EEG, 2) handles the potential challenge with different amplitude scaling within multichannel signals, and 3) can detect directed information flow within a subsecond time scale in nonstationary multichannel EEG datasets. This method holds promise for estimating directed interactions between scalp EEG channels that are commonly affected by the confounding impact of mutual cortical sources.
Augmented Reality Navigation With Automatic Marker-Free Image Registration Using 3-D Image Overlay for Dental Surgery

Computer-assisted oral and maxillofacial surgery (OMS) has been rapidly evolving since the last decade. State-of-the-art surgical navigation in OMS still suffers from bulky tracking sensors, troublesome image registration procedures, patient movement, loss of depth perception in visual guidance, and low navigation accuracy. We present an augmented reality navigation system with automatic marker-free image registration using 3-D image overlay and stereo tracking for dental surgery. A customized stereo camera is designed to track both the patient and instrument. Image registration is performed by patient tracking and real-time 3-D contour matching, without requiring any fiducial and reference markers. Real-time autostereoscopic 3-D imaging is implemented with the help of a consumer-level graphics processing unit. The resulting 3-D image of the patient's anatomy is overlaid on the surgical site by a half-silvered mirror using image registration and IP-camera registration to guide the surgeon by exposing hidden critical structures. The 3-D image of the surgical instrument is also overlaid over the real one for an augmented display. The 3-D images present both stereo and motion parallax from which depth perception can be obtained. Experiments were performed to evaluate various aspects of the system; the overall image overlay error of the proposed system was 0.71 mm.
Hypnogram and Sleep Parameter Computation From Activity and Cardiovascular Data

The automatic computation of the hypnogram and sleep Parameters, from the data acquired with portable sensors, is a challenging problem with important clinical applications. In this paper, the hypnogram, the sleep efficiency (SE), rapid eye movement (REM), and nonREM (NREM) sleep percentages are automatically estimated from physiological (ECG and respiration) and behavioral (Actigraphy) nocturnal data. Two methods are described; the first deals with the problem of the hypnogram estimation and the second is specifically designed to compute the sleep parameters, outperforming the traditional estimation approach based on the hypnogram. Using an extended set of features the first method achieves an accuracy of 72.8%, 77.4%, and 80.3% in the detection of wakefulness, REM, and NREM states, respectively, and the second an estimation error of 4.3%, 9.8%, and 5.4% for the SE, REM, and NREM percentages, respectively.
A general criterion for synchronization of incrementally dissipative nonlinearly coupled agents

Whereas synchronization (consensus, agreement) in linear networks has been thoroughly studied in recent years up to certain exhaustiveness, reaching a synchrony among general nonlinear agents still remains a hard open problems. In this paper, we propose a general criteria of synchronization in undirected networks with nonlinear nodes, based on the idea of incremental dissipativity. We show that our result implies a number of existing synchronization criteria, employing incremental Lyapunov functions (such as e.g. the sum of squared deviations between the agents' states). At the same time, unlike most of existing results of synchronization, our criterion is applicable not only to linearly coupling maps, but also to a wide class of nonlinear couplings, provided they are anti-symmetric and satisfy a quadratic constraint, which should be in correspondence with the incremental supply rate and the network topology. Our criterion thus allows to extend many existing synchronization criteria to the case of nonlinear couplings; moreover, it can be applied to some classes of agent, uncovered by previous results, among them are nonlinear agents in Lurie form and biochemical oscillators of Goodwin's type.
The LISS—A Public Database of Common Imaging Signs of Lung Diseases for Computer-Aided Detection and Diagnosis Research and Medical Education

Lung computed tomography (CT) imaging signs play important roles in the diagnosis of lung diseases. In this paper, we review the significance of CT imaging signs in disease diagnosis and determine the inclusion criterion of CT scans and CT imaging signs of our database. We develop the software of abnormal regions annotation and design the storage scheme of CT images and annotation data. Then, we present a publicly available database of lung CT imaging signs, called LISS for short, which contains 271 CT scans and 677 abnormal regions in them. The 677 abnormal regions are divided into nine categories of common CT imaging signs of lung disease (CISLs). The ground truth of these CISLs regions and the corresponding categories are provided. Furthermore, to make the database publicly available, all private data in CT scans are eliminated or replaced with provisioned values. The main characteristic of our LISS database is that it is developed from a new perspective of CT imaging signs of lung diseases instead of commonly considered lung nodules. Thus, it is promising to apply to computer-aided detection and diagnosis research and medical education.
Actuator fault diagnosis in an octorotor UAV using sliding modes technique: Theory and experimentation

This paper presents a fault diagnosis strategy for actuators faults in an octorotor unmanned aerial vehicle (UAV) using sliding modes techniques. First, system states are estimated using a second order sliding-mode observer (SOSMO) based on the modified super-twisting algorithm. Second, faults are estimated by considering the system control as an unknown input. After the convergence of the observer, the equivalent output injection is used to estimate the unknown input and thus to identify losses in the actuators. The effectiveness of this approach is illustrated by numerical simulations on Matlab/Simulink, and also a real experimental application on a coaxial octorotor UAV.
Icing detection and identification for unmanned aerial vehicles: Multiple model adaptive estimation

The accretion of ice layers on wings and control surfaces modifies the shape of the aircraft and, consequently, alters performance and controllability of the vehicle. In this paper we propose a multiple model adaptive estimation framework to detect icing affecting unmanned aerial vehicles using the following sensors: pitot tube (airspeed sensor), GPS and IMU. A case-study is provided as support and validation of theoretical results.
Multiscale 3-D + t Intracranial Aneurysmal Flow Vortex Detection

Objective: Characteristics of vortices within intracranial aneurysmal flow patterns have been associated with increased risk of rupture. The classifications of these vortex characteristics are commonly based upon qualitative scores, and are, therefore, subjective to user interpretation. We present a quantitative method for automatic time-resolved characterization of 3-D flow patterns and vortex detection within aneurysms. Methods: Our approach is based upon the combination of kernel deconvolution and Jacobian analysis of the velocity field. The deconvolution approach is accurate in detecting vortex centers but cannot discriminate between vortices and high-shear regions. Therefore, this approach is combined with analysis of the Jacobian of the velocity field. Scale-space theory is used to evaluate aneurysmal flow velocity fields at various scales. Results: The proposed algorithm is applied to computational fluid dynamics and time-resolved 3-D phase-contrast magnetic resonance imaging of aneurysmal flow. Conclusion: Results show that the proposed algorithm efficiently detects, visualizes, and quantifies vortices in intracranial aneurysmal velocity patterns at multiple scales and follows the temporal evolution of these patterns. Significance: Quantitative analysis performed with this method has the potential to reduce interobserver variability in aneurysm classification.
A Novel Region Reconstruction Method for Fluorescence Molecular Tomography

Fluorescence molecular tomography (FMT) could exploit the distribution of fluorescent biomarkers that target tumors accurately and effectively, which enables noninvasive real-time 3-D visualization as well as quantitative analysis of small tumors in small animal studies in vivo. Due to the difficulties of reconstruction, continuous efforts are being made to find more practical and efficient approaches to accurately obtain the characteristics of fluorescent regions inside biological tissues. In this paper, we propose a region reconstruction method for FMT, which is defined as an L1-norm regularization piecewise constant level set approach. The proposed approach adopts a priori information including the sparsity of the fluorescent sources and the fluorescent contrast between the target and background. When the contrast of different fluorescent sources is low to a certain degree, our approach can simultaneously solve the detection and characterization problems for the reconstruction of FMT. To evaluate the performance of the region reconstruction method, numerical phantom experiments and in vivo bead-implanted mouse experiments were performed. The results suggested that the proposed region reconstruction method was able to reconstruct the features of the fluorescent regions accurately and effectively, and the proposed method was able to be feasibly adopted in in vivo application.