Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Frame-Rate Contrast-Enhanced Ultrasound for Velocimetry in the Human Abdominal Aorta

Treatment of abdominal aortic (AA) aneurysms and stenotic lesions may be improved by analyzing their associated blood-flow patterns. Angle-independent blood-flow patterns in the AA can be obtained by combining echo-particle image velocimetry (ePIV) with high-frame-rate (HFR) contrast-enhanced ultrasonography. However, ePIV performance is affected by ultrasound contrast agent (UCA) concentration, microbubble stability, and tissue clutter. In this study, we assessed the influence of acoustic pressure and UCA concentration on image quality for ePIV analysis. We also compared amplitude modulation (AM) and singular value decomposition (SVD) as tissue suppression strategies for ePIV. Fourteen healthy volunteers were imaged in the region of the distal AA. We tested four different UCA bolus volumes (0.25, 0.5, 0.75, and 1.5 mL) and four different acoustic output pressures (mechanical indices: 0.01, 0.03, 0.06, and 0.09). As image quality metrics, we measured contrast-to-background ratio, bubble disruption ratio, and maximum normalized cross-correlation value during ePIV. At mechanical indices ≥ 0.06, we detected severe bubble destruction, suggesting that very low acoustic pressures should be used for ePIV. SVD was able to suppress tissue clutter better than AM. The maximum tracking correlation was affected by both UCA concentration and flow rate, where at high flow rates, lower UCA concentrations resulted in slightly higher correlation values but more signal drop-outs during late diastole. HFR ePIV was successfully performed in the AA of healthy volunteers and shows promise for future studies in patients.
A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks

This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor’s local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system’s true state always resides in each sensor’s bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach.
Interscalenic versus suprascapular nerve block: can the type of block influence short- and long-term outcomes? An observational study.

BACKGROUND While interscalenic nerve block (INB) is still considered the gold standard for shoulder arthroscopy, its postoperative analgesic effectiveness has recently been called into question. Meanwhile, in light of its high-quality postoperative pain relief, a renewed interest has emerged in suprascapular nerve block (SNB). The first aim of our study was to compare the postoperative analgesia effects of these two types of block at two, four and six hours after surgery. We also assessed shoulder functional recovery over a 6-month follow-up period. METHODS All patients requiring arthroscopic shoulder surgery for rotator cuff repair during the study period were enrolled. INB or SNB was performed under ultrasound guidance. The patients underwent general anaesthesia. Numerical rate scores (NRS) at rest and in motion at two, four and six postoperative hours were recorded. RESULTS Over two years, 280 patients were screened. Of these, 136 were excluded. Pain scores after surgery were lower at two hours in INB at rest (0.70±1.50 versus 2.1±2.2; P<0.0067) and after movement (1.0±2.2 versus 2.5±2.3; P=0.01). A significant difference in terms of arm extrarotation degrees at week 6 and month 2 (P<0.01) in SNB was found. CONCLUSIONS INB showed better analgesic efficacy in the immediate postoperative period. Both types of block showed similar results in terms of functional recovery over the six-month follow-up. SNB without motor block seems matched better with ambulatory surgery and with an early rehabilitation program.
Measuring precarious employment in Europe 8 years into the global crisis.

Background The objective of this study is to describe the prevalence of precarious employment in the European Union (EU) using a multidimensional approach, 8 years into the economic crisis (2014). Methods We use data from the Flash Eurobarometer 398 among salaried workers (n = 7702). We calculated the proportion and its 95% confidence intervals (CI 95%) for each of the precarious employment dimensions (not having the ability to exercise rights, vulnerability, disempowerment and temporariness), the prevalence of precarious employment (presenting at least one dimension) and the proportion of workers presenting one, two, three or four dimensions. Results Two out of three workers had a precarious employment. The prevalence of precariousness was higher in Eastern (72.64%; CI 95%: 61.78; 81.34) than in Nordic European countries (51.17%; CI 95%: 44.30; 58.00). The most prevalent dimension was not having the ability to exercise rights (42.39%). Conclusions Precarious employment is an important social determinant of health. Therefore, the EU policy-makers should take into consideration the new forms of employment and legislate accordingly.
Sequential Independent Component Analysis Density Estimation

A problem of multivariate probability density function estimation by exploiting linear independent components analysis (ICA) is addressed. Historically, ICA density estimation was initially proposed under the name projection pursuit density estimation (PPDE) and two basic methods, named forward and backward, were published. We derive a modification of the forward PPDE method, which avoids a computationally demanding optimization involving Monte Carlo sampling of the original method. The results of the experiments show that the proposed method presents an attractive choice for density estimation, which is pronounced for a small number of training observations. Under such conditions, our method usually outperforms model-based Gaussian mixture model. We also found that our method obtained better results than the backward PPDE methods in the situation of nonfactorizable underlying density functions. The proposed method has demonstrated a competitive performance compared with the support vector machine and the extreme learning machine in some real classification tasks.
Quantized Sampled-Data Control for Synchronization of Inertial Neural Networks With Heterogeneous Time-Varying Delays

This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov–Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results.
Ultrawideband, Stable Normal and Cancer Skin Tissue Phantoms for Millimeter-Wave Skin Cancer Imaging

This work introduces new, stable, and broadband skin-equivalent semisolid phantoms for mimicking interactions of millimeter waves with the human skin and skin tumors. Realistic skin phantoms serve as an invaluable tool for exploring the feasibility of new technologies and improving design concepts related to millimeter-wave skin cancer detection methods. Normal and malignant skin tissues are separately mimicked by using appropriate mixtures of deionized water, oil, gelatin powder, formaldehyde, TX-150 (a gelling agent, widely referred to as “super stuff”), and detergent. The dielectric properties of the phantoms are characterized over the frequency band of 0.5–50 GHz using a slim-form open-ended coaxial probe in conjunction with a millimeter-wave vector network analyzer. The measured permittivity results show excellent match with ex vivo, fresh skin (both normal and malignant) permittivities determined in our prior work over the entire frequency range. This work results in the closest match among all phantoms reported in the literature to surrogate human skin tissues. The stability of dielectric properties over time is also investigated. The phantoms demonstrate long-term stability (up to 7 months was investigated). In addition, the penetration depth of millimeter waves into normal and malignant skin phantoms is calculated. It is determined that millimeter waves penetrate the human skin deep enough (0.6 mm on average at 50 GHz) to affect the majority of the epidermis and dermis skin structures.
Autonomic Nervous System Measurement in Hyperbaric Environments Using ECG and PPG Signals

The main aim of this paper was to characterize the Autonomic Nervous System response in hyperbaric environments using electrocardiogram (ECG) and pulse-photoplethysmogram (PPG) signals. To that end, 26 subjects were introduced into a hyperbaric chamber and five stages with different atmospheric pressures (1 atm; descent to 3 and 5 atm; ascent to 3 and 1 atm) were recorded. Respiratory information was extracted from the ECG and PPG signals and a combined respiratory rate was studied. This information was also used to analyze Heart Rate Variability (HRV) and Pulse Rate Variability (PRV). The database was cleaned by eliminating those cases where the respiratory rate dropped into the low frequency band (LF: 0.04–0.15 Hz) and those in which there was a discrepancy between the respiratory rates estimated using the ECG and PPG signals. Classical temporal and frequency indices were calculated in such cases. The ECG results showed a time-related dependency, with the heart rate and sympathetic markers (normalized power in LF and LF/HF ratio) decreasing as more time was spent inside the hyperbaric environment. A dependence between the atmospheric pressure and the parasympathetic response, as reflected in the high-frequency band power (HF: 0.15–0.40 Hz), was also found, with power increasing with atmospheric pressure. The combined respiratory rate also reached a maximum in the deepest stage; thus, highlighting a significant difference between this stage and the first one. The PPG data gave similar findings and also allowed the oxygen saturation to be computed; therefore, we propose the use of this signal for future studies in hyperbaric environments.
Feedback Control of Functional Electrical Stimulation for 2-D Arm Reaching Movements

Functional electrical stimulation (FES) can be used as a neuroprosthesis in which muscles are stimulated by electrical pulses to compensate for the loss of voluntary movement control. Modulating the stimulation intensities to reliably generate movements is a challenging control problem. This paper introduces a feedback controller for a multi-muscle FES system to control hand movements in a 2-D (table-top) task space. This feedback controller is based on a recent human motor control model, which uses muscle synergies to simplify its calculations and improve the performance. This synergy-based controller employs direct relations between the muscle synergies and the produced hand force, therefore allowing for the real-time calculation of six muscle stimulation levels required to reach an arbitrary target. The experimental results show that this control scheme can perform arbitrary point-to-point reaching tasks in the 2-D task space in real time, with an average of ~2 cm final hand position error from the specified targets. The success of this prototype demonstrates the potential of the proposed method for the feedback control of functional tasks with FES.
A 3‐year longitudinal study of skeletal effects and growth in children after kidney transplantation

This prospective study investigated growth and skeletal development for 3 years after kidney transplantation in pediatric patients, 3.4‐15.0 years of age. Growth, BMD, bone resorption markers (CTX and TRACP5b), bone formation markers (PINP, ALP, and osteocalcin), PTH, and vitamin D were assessed at start, 3, 12, and 36 months after transplantation. Median GFR was 63 (range 37‐96) mL/min/1.73 m2 after 3 years. The median height SDS increased from −1.7 to −1.1, and median BMI SDS increased from −0.1 to 0.6 over 3 years, which shows that transplantation had a favorable outcome on growth. Fat mass increased after transplantation at all time points, whereas lean mass increased after 1 year and 3 years. Total BMC increased at all time points. No changes were observed for total BMD. Bone resorption markers decreased initially after 3 months and remained stable throughout the study, whereas the bone formation markers decreased initially, but successively increased over the study period. In conclusion, this study demonstrates that height SDS and BMI SDS increased, along with the increased formation markers that reveal a positive bone acquisition after kidney transplantation, which was reflected by the significant increase in total body BMC.
Time Course of Treatment for Primary Enuresis With Overactive Bladder

Purpose To characterize the course of treatment for nonmonosymptomatic enuresis with overactive bladder (OAB) in a real clinical setting. Methods Data from 111 OAB patients with moderate to severe enuresis were analyzed. The baseline analysis included a questionnaire, voiding diary, uroflowmetry with postvoid residual urine measurement, and plain abdominal radiography of the kidneys, ureters, and bladder (KUB). Following standard urotherapy for 1 month, anticholinergic medication was administered with or without laxatives. Desmopressin was added if there was a partial response to OAB. Patients were followed every 3 months to evaluate the status of OAB and enuresis. Multivariate analysis was performed to identify predictors associated with the lack of complete response (CR) in enuresis at 12 months. Results Following 12 months of treatment, 64% and 88% of patients experienced at least partial response in enuresis and OAB, respectively. Urgency improved more quickly than enuresis, supporting the need to address daytime symptoms before enuresis. Seventy-nine patients (71%) had fecal impaction on KUB and/or subjective constipation. The combination of anticholinergics with either laxatives or desmopressin fared better than anticholinergics alone. Daytime incontinence and anticholinergics-only treatment were associated with a lack of CR during 12 months of treatment. Conclusions The data confirmed the validity of addressing OAB before treating enuresis. The results of this study also highlight the need to address fecal impaction. Patients should be counseled about the need for a prolonged course of treatment before starting treatment. Anticholinergics should be accompanied with either desmopressin or laxatives for better control of enuresis.
Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image

Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are mostly based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.
A Radiomics Approach With CNN for Shear-Wave Elastography Breast Tumor Classification

This paper proposes a segmentation-free radiomics method to classify malignant and benign breast tumors with shear-wave elastography (SWE) data. The method is targeted to integrate the advantage of both SWE in providing important elastic with morphology information and convolutional neural network (CNN) in automatic feature extraction and accurate classification. Compared to traditional methods, the proposed method is designed to directly extract features from the dataset without the prerequisite of segmentation and manual operation. This can keep the peri-tumor information, which is lost by segmentation-based methods. With the proposed model trained on 540 images (318 of malignant breast tumors and 222 of benign breast tumors, respectively), an accuracy of 95.8%, a sensitivity of 96.2%, and a specificity of 95.7% was obtained for the final test. The superior performances compared to the existing state-of-the-art methods and its automatic nature both demonstrate that the proposed method has a great potential to be applied to clinical computer-aided diagnosis of breast cancer.
Been there, done that, so what's next for arm and hand rehabilitation in stroke?

BACKGROUND The extraordinary advances in technology such as body-worn sensors, health information technologies, technological advances in neuroimaging, and computational approaches to predictive modelling using biomarkers offers considerable promise to literally transform our thinking, our approach to the problem, and the design of future clinical trials about arm and hand rehabilitation after stroke. OBJECTIVE To provide a focused review that considers the past, present and future of arm and hand rehabilitation in stroke. METHOD We organized this perspective into three parts: 1) Past- we summarize the past decade of the clinical trial enterprise in neurorehabilitation, 2) Present- we provide a brief review of three research areas where mechanistic studies that rely on uniquely human neural circuits provide a basis for promising intervention tools, and 3) Future-we highlight three unique research domains that are likely to provide the biggest impact on the future of post-stroke arm and hand recovery. RESULTS The past has not been a complete failure- in particular, the EXCITE RCT put arm and hand rehabilitation on the map. Unfortunately, the majority of clinical trials that followed were based on an immature science of neurorehabilitation. We got drawn in by the seductive preclinical animal model work which suggested that dose and intensity of task-oriented training was the most important ingredient for fostering recovery in humans. While dose, and intensity are clearly important, they are of little value unless the stroke survivor is engaged, motivated, and the neural infrastructure provides enough resource to allow the recovery process. Recently, we noticed an increase in mechanistic and theory-driven studies, findings from which will not only advance our understanding of critical brain-behavior mechanisms, but will provide a more mature science moving into the future. CONCLUSIONS The good news is that there is evidence that we learned from the past and have invented a future that appears to be much more exciting and promising than the past.
Chapter 6 Adequacy of Haemodialysis in UK Adult Patients in 2016: National and Centre-specific Analyses

BACKGROUND Outcome in patients treated with haemodialysis (HD) is influenced by the delivered dose of dialysis. The UK Renal Association (RA) publishes clinical practice guidelines which include recommendations for dialysis dose. The urea reduction ratio (URR) is a widely used measure of dialysis dose. AIM To determine the extent to which patients received the recommended dose of HD in the UK. METHODS All seventy-two UK renal centres submitted data to the UK Renal Registry (UKRR). Two groups of patients were included in the analyses: the prevalent patient population on 31st December 2010 and the incident patient population for 2010. Centres returning data on 65%) increased from 56% in 1998 to 86% in 2010. There was persistent variation observed between centres, with 19 centres attaining the RA clinical practice guideline in >90% of patients and 39 centres attaining the guideline in 70-90%. The overall proportion of prevalent patients with a URR >65% has continued to improve over time. CONCLUSIONS The delivered dose of HD for patients with established renal failure has increased over the last decade. Whilst the majority of UK patients achieved the target URR there was considerable variation between centres in the percentage of patients achieving the guideline.
Dynamical and Static Multisynchronization of Coupled Multistable Neural Networks via Impulsive Control

This paper investigates the dynamical multisynchronization and static multisynchronization problem for delayed coupled multistable neural networks with fixed and switching topologies. To begin with, a class of activation functions as well as several sufficient conditions are introduced to ensure that every subnetwork has multiple equilibrium states. By constructing an appropriate Lyapunov function and by employing impulsive control theory and the average impulsive interval method, several sufficient conditions for multisynchronization in terms of linear matrix inequalities (LMIs) are obtained. Moreover, a unified impulsive controller is designed by means of the established LMIs. Finally, a numerical example is presented to demonstrate the effectiveness of the presented impulsive control strategy.