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Systemic lupus erythematosus (SLE) is a complex, multi-system autoimmune disease of unclear etiology that causes significant morbidity and, in severely affected patients, early mortality. Despite efforts from academic and private research entities, pharmaceutical companies, and patient advocacy groups, and hundreds of millions of dollars in spending, numerous gaps in care still exist. A digital therapeutic platform is described that uses self-tracking technology, analytics, and telehealth coaching to identify and remove possible dietary and/or other lifestyle triggers of SLE. A clinical proof of concept study was performed with 18 SLE patients over a 12 week program. All participants reported improvements in their symptoms, including pain, fatigue, digestive, and other physical symptoms.Clinical Relevance- This study demonstrates the technical and clinical feasibility of a digital therapeutic platform to improve the health-related quality of life in patients with systemic lupus erythematosus.

Sleep is an essential process in our life, which covers 1/3 of our lifetime. But this process can be affected by disorders producing serious consequences at physiological and behavioral level. One of the major indexes connected to the sleep disorders is the dynamic of the sleep macrostructure that is used for the assessment of sleep quality. Beyond sleep macrostructure, recently attention is also given to a finer structure of sleep called Cyclic Alternating Pattern (CAP). CAP is composed by short cortical events (A-phases), where some transition processes can be observed. With the aim to unveil properties of this transition phenomenon, in this work, we present a wavelet singularity analysis of the EEG signal during the onset and offset of A-phases. The results showed that EEG signal presents significant differences between A-phases and activity of background when the average singularity is considered. This finding can help both in better delineating the A-phases of CAP sleep and in understanding the mechanisms behind the CAP dynamics.

Recent advancements in computation hardware and software has fueled the adoption of in silico techniques, such as density functional theory (DFT), as an investigative tool in materials' science. Hence, this study employs DFT to investigate the properties of monolayer SnS2 as function of transition metal dopant atoms. The transition metal dopants considered are T1, V, Cr, Mn, Fe, Co, Ni, Cu and Zn. Only cation substitutional doping has been considered and the doping profiles investigated were single-site and multi-site cation substitutional doping. S-rich conditions were found to favor both doping profiles with Ni and Ti not inducing any magnetic moment. This study provides insights on how in silico techniques can be adopted in material's research to evaluate dopant influences on the properties of the host.

Dementia is a growing global challenge that is difficult to treat. Pharmaceutical treatment approaches have had limited success, leading to an increased focus on nonpharmaceutical approaches to the treatment of dementia. A clinical pilot study was performed to evaluate whether ReminX digital therapeutic software, based on reminiscence therapy, has the potential to improve emotional functioning in patients with Alzheimer's disease and related dementias. ReminX allows the uploading of pictures and narration to create slideshow stories depicting important moments in the patient's life. Fourteen patients were evaluated in their home, and their emotional health was assessed both before and after using ReminX. Results indicated that patients reported significantly less anxiety, depression, and overall emotional distress after having viewed their story. Furthermore, patient's caregivers also reported that the patient appeared less emotionally distressed. The effect sizes for the significant results ranged from 0.76 to 0.91. These effect sizes, which were larger than anticipated, suggest that digitally-delivered reminiscence therapy can have an immediate and positive impact on emotional functioning in patients with dementia. In addition, the accessibility, scalability, and ease of use of the software platform suggests that this technology holds great promise as a product for use in both the home and senior care settings.

Since its inception by Gauss, the least-squares problem has frequently arisen in science, mathematics, and engineering. Iterative methods, such as Conjugate Gradient Normal Residual (CGNR), have been popular for solving sparse least-squares problems, but have historically been regarded as undesirable for dense applications due to poor convergence. We contend that this traditional "common knowledge" should be reexamined. Preconditioned CGNR, and perhaps other iterative methods, should be considered alongside standard methods when addressing large dense least-squares problems. In this paper we present TNT, a dynamite method for solving large dense least-squares problems. TNT implements a Cholesky preconditioner for the CGNR fast iterative method. The Cholesky factorization provides a preconditioner that, in the absence of round-off error, would yield convergence in a single iteration. Through this preconditioner and good parallel scaling, TNT provides improved performance over traditional least-squares solvers allowing for accelerated investigations of scientific and engineering problems. We compare a parallel implementation of TNT to parallel implementations of other conventional methods, including the normal equations and the QR method. For the small systems tested (15000 15000 or smaller), it is shown that TNT is capable of producing smaller solution errors and executing up to 16 faster than the other tested methods. We then apply TNT to a representative rock magnetism inversion problem where it yields the best solution accuracy and execution time of all tested methods.

Raman spectroscopy of biological tissue presents fluorescence background, an undesirable effect that generates false Raman intensities. This paper proposes the application of the Empirical Mode Decomposition (EMD) method to baseline correction. EMD is a suitable approach since it is an adaptive signal processing method for nonlinear and non-stationary signal analysis that does not require parameters selection such as polynomial methods. EMD performance was assessed through synthetic Raman spectra with different signal to noise ratio (SNR). The correlation coefficient between synthetic Raman spectra and the recovered one after EMD denoising was higher than 0.92. Additionally, twenty Raman spectra from skin were used to evaluate EMD performance and the results were compared with Vancouver Raman algorithm (VRA). The comparison resulted in a mean square error (MSE) of 0.001554. High correlation coefficient using synthetic spectra and low MSE in the comparison between EMD and VRA suggest that EMD could be an effective method to remove fluorescence background in biological Raman spectra.

Evolutionary algorithms are powerful tools for optimizing problems, and their performance in various fields is a continuous subject of research. Network reconstruction is the problem of trying to discover the original structure of an existing network using limited information. In this paper, we attempt to explore the ability of evolutionary algorithms to reconstruct networks by using the degree of each node as the available information. To achieve this, we introduce a novel method of representing networks with each edge being a value that corresponds to the likelihood of its existence. Moreover, we design two fitness functions that only use the degree of each node to determine the reconstruction quality, and analyze and compare them using fitness landscape and schema analysis for small networks, and verify the performance using two evolutionary algorithms for larger networks.

A-phases are transient events with a length between 2 and 60 s that can be observed in the EEG during NREM sleep. Recent studies have characterized the properties of the A-phases based on the spectral components of the EEG signal. These studies have given interesting insight about the Aphases and have helped to develop automatic algorithms for their detection. The characterization includes the evaluation of only the typical bands of the EEG signal, including Delta (0.03 Hz - 4 Hz), Theta (4 Hz - 8 Hz), Alpha (8 Hz - 12 Hz) and Beta (12 Hz - 30 Hz); however, to our knowledge, there are not any sleep EEG studies during the A-phases involving frequencies higher than the Beta band (e.g., the Gamma band (30 Hz - 100 Hz)). This paper focuses on the characterization of the spectral components of the A-phases on EEG activity by means of Principal Component Analysis, considering the full bandwidth of the EEG signal (up to 100 Hz). The results showed that frequency components above 50 Hz exist, especially during the A3-phase. Thus the results suggest that Gamma activity can be found during the NREM sleep process.

The sleep phenomenon is a complex process that involves fluctuations of autonomic functions such as the blood pressure, temperature and brain function. These fluctuations change their properties through the different sleep stages with specific relations among the different systems. In order to understand the relation between the cardiovascular and central nervous system at the different sleep stages, we applied different non-linear methods to the energy of electroencephalographic signal (EEG) and the heart rate fluctuations. The EEG was divided in the Delta, Theta, Alpha and Beta frequency bands and the mean energy of these bands was computed at each heart rate interval. Thus, the non-linear relation was evaluated between the energy of the EEG bands and the heart rate fluctuations using Cross-Correlation, Cross-Sample Entropy and Recurrence Quantification Analysis in segments of 5 minutes grouped by sleep stage. The results showed that a relation exists between the changes of the energy in the Delta band and the Heart rate fluctuations.

In this paper, we propose a differential evolution algorithm to solve the resource investment problem (RIP) with tardiness, which is one class of project scheduling problems. The tardiness of a project is allowed with defined penalty. We adapt a self-adaptive differential evolution algorithm combined with a local search operator to solve this problem. The performance of this algorithm is evaluated using 450 problem instances. The results show that these instances can reach their optimal solutions in a fixed number of schedules evaluated. The experimental results are quite satisfactory.

In this paper, the multi-agent genetic algorithm (MAGA) is combined with the variable neighborhood search (VNS) to solve resource investment project scheduling problems (RIPSPs). An agent, coded by a valid activity list and a capacity list, represents a candidate solution to the RIPSPs. All agents live in a lattice-like environment, with each agent fixed on a lattice point. To increase energies, a series of operators, namely crossover, mutation, competition, self-learning and a VNS, are designed. The effectiveness of the algorithm is demonstrated through experiments on Mohring instances, synthetic instances and generated instances of J10, J14 and J20. The tests results are satisfactory.

In this paper, we propose a differential evolution algorithm with local search to solve the resource investment project scheduling problems (RIPSPs), labeled as DE LS -RIPSP. Project tardiness is not permitted during the process of optimization. DE LS -RIPSP improves the population quality by changing existing chromosomes to those with better fitness using the local search operator in order to reduce resource cost. The performance of DE LS -RIPSP is validated on 450 benchmark problems, including Mohring instances and ProGen instances with 10, 14 and 20 non-dummy activities. The percentages of optimal solutions under a fixed number of evaluations are reported. The experimental results illustrate the effectiveness of the proposed algorithm and its potential for solving RIPSPs.

There is continuing interest in targeted transcranial stimulation of deep brain structures using techniques such as low intensity focused ultrasound (LIFU). Accurate transcranial targeting and dosage of acoustic energy onto target neural structure requires quantification of the acoustic effect the skull has on the ultrasound wave. The objective of this work is to assess the effect of transcranial passage, both in terms of attenuation and orientation of an incident acoustic wave, in the gyrencephalic Gottingen minipig. Precision measurements of the acoustic transit of the stereotactically addressed intact-skulls resulted in a consistently measured acoustic loss of about 8.5 dB from the skull transition, corresponding to an intensity transmission coefficient of 0.1298 ± 0.00990. Negligible spatial shift in the acoustic focus was observed. These results indicate that the Gottingen minipig may be suitable for studies of targeted transcranial neuromodulation due to the predictable acoustic transmission measured from the intact skull.

Studies with low intensity focused ultrasound (LIFU) and other related techniques have recently demonstrated the potential for ultrasound to reversibly modulate neural circuits in a number of different in vivo models. However, accurate acoustic targeting can be complicated by the attenuation and distortion of the acoustic beam during transcranial passage through the cranium. This can potentially complicate basic studies of the effects of targeted ultrasonic neurostimulation. An alternative intervention strategy is to develop ultrasonic neurostimulator probes small enough to be minimally-invasively implanted directly adjacent to target neural structures. Two different configurations of PZT-based low frequency microtransducers were designed, fabricated, and evaluated for such a strategy. Acoustic testing demonstrated evenly collimated acoustic radiation profiles, and a pilot study in a small animal model demonstrated the overall feasibility of this approach.

This work aims to investigate sleep microstructure as expressed by Cyclic Alternating Pattern (CAP), and its possible alterations in pathological sleep. Three groups, of 10 subjects each, are considered: a) normal sleep, b) psychophysiological insomnia, and c) sleep misperception. One night sleep PSG and sleep macro- micro structure annotations were available per subject. The statistical properties and the dynamics of CAP events are in focus. Multiscale and non-linear methods are presented for the analysis of the microstructure event time series, applied for each type of CAP events, and their combination. The results suggest that a) both types of insomnia present CAP differences from normal sleep related to hyperarousal, b) sleep misperception presents more extensive differences from normal, potentially reflecting multiple sleep mechanisms, c) there are differences between the two types of insomnia as regard to the intertwining of events of different subtypes. The analysis constitutes a contribution towards new markers for the quantitative characterization of insomnia, and its subtypes.

This work is part of a continuous effort to achieve characterization of tissue from auto-fluorescence measurements. One particular problem is the estimation of the number of components in a sample from multi-spectral Fluorescence Lifetime Imaging Data (m-FLIM). The proposed method is based on a two-step iterative procedure, where first a blind end-member and abundance extraction algorithm is employed, followed by an evaluation of the resulting end-members by solving an optimal approximation problem. A threshold method is employed to evaluate if the extracted end-members are nonredundant. The validation of the proposal is performed by 3 m-FLIM data sets from post-mortem human coronary artery samples, where the results obtained matched the qualitative description provided by histopathology slides.

Prostate imaging commonly performed using transrectal ultrasound (TRUS), has been unable to image the prostate in real-time 3D. Transurethral ultrasound (TUUS), a new method of prostate imaging, may provide a solution. We developed a 3D reconstruction method for transurethral ultrasound using a radially-phased catheter transducer, and performed an in vitro validation and comparison with transrectal ultrasound (TRUS) and magnetic resonance imaging (MRI). The 3D reconstruction method was able to generate TUUS volumes in a near real-time fashion. Calculated volumes of a prostate phantom were similar in all three methods. Inserted fiducial markers were better visualized in TUUS. MR-TUUS fusion was demonstrated using current registration algorithms optimized for TRUS. Future improvements to the volume reconstruction and MR fusion accuracy may enable TUUS to become an aid in prostate cancer diagnosis.

A novel method for approximate string matching with applications to bioinformatics is presented in this paper. Unlike most methods in the literature, the proposed method does not depend on the computation of the edit distance between two sequences, but uses instead a similarity index obtained by applying the phase correlation method. The resulting algorithm provides a finer control over the false positive rate, allowing users to pick out relevant matchings in less time, and can be applied for both offline and online processing.

Although surgical robotic systems provide several advantages over conventional minimally invasive techniques, they are limited by a lack of tactile feedback. Recent research efforts have successfully integrated tactile feedback components onto surgical robotic systems, and have shown significant improvement to surgical control during in vitro experiments. The primary barrier to the adoption of tactile feedback in clinical use is the unavailability of suitable force sensing technologies. This paper describes the design and fabrication of a thin-film capacitive force sensor array that is intended for integration with tactile feedback systems. This capacitive force sensing technology could provide precise, high-sensitivity, real-time responses to both static and dynamic loads. Capacitive force sensors were designed to operate with optimal sensitivity and dynamic range in the range of forces typical in minimally invasive surgery (0-40 N). Initial results validate the fabrication of these capacitive force-sensing arrays. We report 16.3 pF and 146 pF for 1-mm(2) and 9-mm(2) capacitive areas, respectively, whose values are within 3% of theoretical predictions.

This study proposes an automatic method for the sleep-wake staging in normal and pathologic sleep based only on respiratory effort acquired from a Pressure Bed Sensor (PBS). Motion and respiratory movements were obtained through a PBS and sleep-wake staging was evaluated from those time series. 20 all night polysomnographies, with annotations, used as gold standard and the time series coming from the PBS were used to develop and to evaluate the automatic wake-sleep staging. The database was built up by: 10 healthy subjects and 10 patients with severe sleep apnea. The agreement of the statistical measures between the automatic classification and the human scoring were: 83.59 ± 6.79 of sensitivity, 83.60 ± 15.13 of specificity and 81.91 ± 6.36 of accuracy. These results suggest that some important indexes, such as sleep efficiency, could be computed through a contactless technique

Diffusion-weighted images are increasingly being employed in conjunction with T2-weighted images for accurate diagnosis of cervical cancer. However, these images suffer from eddy-current related and magnetic field inhomogeneity induced distortions. This paper presents an investigation into the reverse gradient technique to correct distortions in diffusion-weighted echo-planar images of human cervix. We hypothesize that the correction aligns the diffusion-weighted images with the T2-weighted images. To allow for any misalignments and to evaluate the correction, a normalized cross-correlation driven template matching strategy was employed to assess the correlation between the corrected image and the T2-weighted image. For b = 0 smm−2 images, mean correlation values of 0.72±0.14 for regions-of-interest drawn around the cervix for 10 patients were achieved. We also found that our hypothesis was most valid in regions encompassing the cervix with a misalignment of 4.4±3.4mm. Results are presented for images acquired at a field-of-view of 100mm using an endovaginal coil developed in-house for cervix imaging at 3T which is the first of its kind in the world.

The topic of deep-learning has recently received considerable attention in the machine learning research community, having great potential to liberate computer scientists from hand-engineering training datasets, because the method can learn the desired features automatically. This is particularly beneficial in medical research applications of machine learning, where getting good hand labelling of data is especially expensive. We propose application of a single-layer sparse-auto encoder to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for fully automatic classification of tissue types in a large unlabelled dataset with minimal human interference -- in a manner similar to data-mining. DCE-MRI analysis, looking at the change of the MR contrast-agent concentration over successively acquired images, is time-series analysis. We analyse the change of brightness (which is related to the contrast-agent concentration) of the DCE-MRI images over time to classify different tissue types in the images. Therefore our system is an application of an auto encoder to time-series analysis while the demonstrated result and further possible successive application areas are in computer vision. We discuss the important factors affecting performance of the system in applying the auto encoder to the time-series analysis of DCE-MRI medical image data.

The paper addresses the problem of identification of fluorescent molecules, or fluorophores, in biological samples obtained from time-resolved fluorescence microscopy. The contribution of this work is an algorithm, based on particle filter optimization, that solves the spectral unmixing problem for more than two fluorophores even when they present highly overlapping spectra at different wavelengths. The estimation of the proportional contributions of the elements in the sample is carried out by a Bayesianmethodology. Validation is done by using synthetic mixtures with two components and ex-vivo samples from human atherosclerotic tissue.

This paper presents the evaluation of the accuracy of an elastic registration algorithm, based on the particle filter and an optical flow process. The algorithm is applied in brain CT and MRI simulated image datasets, and MRI images from a real clinical radiotherapy case. To validate registration accuracy, standard indices for registration accuracy assessment were calculated: the dice similarity coefficient (DICE), the average symmetric distance (ASD) and the maximal distance between pixels (Dmax). The results showed that this registration process has good accuracy, both qualitatively and quantitatively, suggesting that this method may be considered as a good new option for radiotherapy applications like patient's follow up treatment.

The aim of this study is to implement a high-accuracy automatic detector of the Cyclic Alternating Pattern (CAP) during sleep. EEG data from four healthy subjects were used. Both the C4-A1 and the F4-C4 leads were analyzed for this study. Seven features were extracted from each of the two leads and two separate studies were performed for each set of descriptors. For both sets, a Support Vector Machine was trained and tested on the data with the Leave One Out cross-validation method. The two final classifications obtained on the two sets were merged, by considering a CAP A phase scored only if it had been recognized both on the central and on the frontal lead. The length of the A phase was then determined by the result on the fronto-central lead. This method leads to encouraging results, with a classification sensitivity on the whole dataset equal to 73.82%, specificity equal to 85.93%, accuracy equal to 84,05% and Cohen's kappa equal to 0.50.

We present a novel system for imaging burns and other lesions of human skin tissue utilizing the acute sensitivity of THz radiation to water concentration. It is a THz impulse radar that carries out reflective, 2D imaging with a photoconductive-switch wideband illuminator, a Schottky-rectifying gated receiver, and mechanical scanning. It has been used to image burns with ~2 mm spatial resolution in ≈5 min over 1 KPixel and 1 sq inch.

A complete glove-based master-slave tactile feedback system was developed to provide users with a remote sense of touch. The system features a force-sensing master glove with piezoresistive force sensors mounted at each finger tip, and a pressure-transmitting slave glove with silicone-based pneumatically controlled balloon actuators, mounted at each finger tip on another hand. A control system translates forces detected on the master glove, either worn by a user or mounted on a robotic hand, to discrete pressure levels at the fingers of another user. System tests demonstrated that users could accurately identify the correct finger and detect three simultaneous finger stimuli with 99.3% and 90.2% accuracy, respectively, when the subjects were located in separate rooms. The glove-based tactile feedback system may have application to virtual reality, rehabilitation, remote surgery, medical simulation, robotic assembly, and military robotics.

In the present work we apply a fully automatic procedure to the analysis of signal coming from a sensorized T-shit, worn during the night, for sleep evaluation. The goodness and reliability of the signals recorded trough the T-shirt was previously tested, while the employed algorithms for feature extraction and sleep classification were previously developed on standard ECG recordings and the obtained classification was compared to the standard clinical practice based on polysomnography (PSG). In the present work we combined T-shirt recordings and automatic classification and could obtain reliable sleep profiles, i.e. the sleep classification in WAKE, REM (rapid eye movement) and NREM stages, based on heart rate variability (HRV), respiration and movement signals.

A tactile feedback vest was developed to provide sensory information to patients with balance disorders. The system was designed to detect levels of imbalance that are imperceptible to patients with balance deficit, and to provide intuitive and appreciable sensory feedback that allows for rapid balance correction. Polydimethylsiloxane (PDMS) based pneumatic actuators were clustered on the ventral, dorsal, left and right surfaces of the tactile vest to provide multi-directional sensory feedback. Two biaxial accelerometers were mounted near the shoulder to measure linear and angular acceleration of the upper torso, and a system controller that regulates communication between the sensors and actuators was developed. Actuator deflection was characterized to optimize tactile feedback. Average deflections of 2.6 mm and 7.1 mm were recorded with input pressures of 1.6 psi and 4.0 psi, respectively. In future studies, human perceptual testing will be performed to optimize the system for clinical use.