Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupling Myocardium and Vortex Dynamics in Diverging-Wave Echocardiography

Echocardiography is widely used to provide critical left ventricular indices describing myocardial motion and blood inflow velocity. Tissue motion and blood flow are strongly connected and interdependent in the ventricle. During cardiac relaxation, rapid filling leads to the formation of a vortical blood flow pattern. In this paper, we introduce a high-frame-rate method to track vortex dynamics alongside myocardium motion, in a single heartbeat. Cardiac triplex imaging (B-mode + tissue Doppler + color Doppler) was obtained by insonating the left ventricle with diverging waves. We used coherent compounding with integrated motion compensation to obtain high-quality B-mode images. Tissue Doppler was retrieved and the septal and lateral velocities of the mitral annulus were deduced. A rate of ~80 triplex images/s was obtained. Vortex dynamics was analyzed by Doppler vortography. Blood vortex signature maps were used to track the vortex and compute core vorticities. The sequence was implemented in a Verasonics scanner with a 2.5-MHz phased array and tested in vivo in 12 healthy volunteers. Two main peaks appeared on the vorticity curves. These peaks were synchronized with the mitral inflow velocities with a small delay. We observed a relationship between the tissue and vortex waveforms, though also with a delay, which denoted the lag between the wall and the flow motion. Clinical diastolic indices combining basal and mitral inflow velocities (E/A ratio and E/ $e^\prime $ ratio) were determined and compared with those measured using a conventional ultrasound scanner; a good correlation was obtained ( $r^{2} = 0.96$ ). High-frame-rate Doppler echocardiography enabled us to retrieve time-resolved dynamics of the myocardium and vortex flow within the same cardiac cycle. Coupling wall-flow analysis could be of clinical relevance for early diagnosis of filling impairment.
Learning From Multiple Imperfect Instructors in Sensor Networks

This paper presents a sequential learning framework for sensors in a network, where a few sensors assume the role of an instructor to train other sensors in the network. The instructors provide estimated labels for measurements of new sensors. These labels are possibly noisy, because a classifier of the instructor may not be perfect. A recursive density estimator is proposed to obtain the true measurement model (i.e., the observation density conditioned on the label) in spite of the training with noisy labels. Specifically, this paper answers the question “Can a sensor train other sensors? ”, provides necessary conditions for sensors to act as instructors, presents a sequential learning framework using recursive nonparametric kernel density estimation, and provides a convergence rate for the expected error in an observation density. The underlying concepts are illustrated and validated with simulation results.
CBL1‐CIPK26‐mediated phosphorylation enhances activity of the NADPH oxidase RBOHC, but is dispensable for root hair growth

Root hairs (RH) are tip growing polarized cells aiding the uptake of nutrients and water into plants. RH differentiation involves the interplay of various hormones and second messengers. Tightly regulated production of reactive oxygen species by the NADPH oxidase RBOHC crucially functions in RH differentiation and Ca2+‐dependent phosphorylation has been implemented in these processes. However, the kinases regulating RBOHC remained enigmatic. Here we identify CBL1‐CIPK26 Ca2+ sensor‐kinase complexes as modulators of RBOHC activity. Combined genetic, cell biological and biochemical analyses reveal synergistic function of CIPK26‐mediated phosphorylation and Ca2+ binding for RBOHC activation. Complementation of rbohC mutant RH phenotypes by a S318/322 phosphorylation deficient RBOHC version suggests flexible and alternating phosphorylation patterns as mechanism fine‐tuning ROS production in RH development.
A two‐phase strategy for control constrained elliptic optimal control problems

Elliptic optimal control problems with pointwise box constraints on the control are considered. To numerically solve elliptic optimal control problems with pointwise box constraints on the control, an inexact alternating direction method of multipliers (iADMM) is first proposed on the continuous level with the aim of solving discretized problems with moderate accuracy. Then, the standard piecewise linear finite element is employed to discretize the related subproblems appearing in each iteration of the iADMM algorithm. Such approach will give us the freedom to discretize two inner subproblems of the iADMM algorithm by different discretized scheme, respectively. More importantly, it should be emphasized that the discretized version of the iADMM algorithm can be regarded as a modification of the inexact semiproximal ADMM (isPADMM) algorithm. In order to obtain more accurate solution, the primal‐dual active set method is used as a postprocessor of the isPADMM. Numerical results not only show that the isPADMM and the two‐phase strategy are highly efficient but also show the mesh independence of the isPADMM.
High-Order Convolutional Neural Network Architecture for Predicting DNA-Protein Binding Sites

Although Deep learning algorithms have outperformed conventional methods in predicting the sequence specificities of DNA-protein binding, they lack to consider the dependencies among nucleotides and the diverse binding lengths for different transcription factors (TFs). To address the above two limitations simultaneously, in this paper, we propose a high-order convolutional neural network architecture (HOCNN), which employs a high-order encoding method to build high-order dependencies among nucleotides, and a multi-scale convolutional layer to capture the motif features of different length. The experimental results on real ChIP-seq datasets show that the proposed method outperforms the state-of-the-art deep learning method (DeepBind) in the motif discovery task. In addition, we provide further insights about the importance of introducing additional convolutional kernels and the degeneration problem of importing high-order in the motif discovery task.
Levels of polychlorinated biphenyls in human milk samples in European countries

Abstract Polychlorinated biphenyls (PCBs) are persistent pollutants, harmful to human health, which enter the human body mainly through food and bind to body fat. For these reasons their use in most countries is prohibited. Human milk has an advantage over other types of human samples in measuring human exposure to PCBs, as it is obtained with non-invasive sampling methods. In Europe, including Croatia, PCB levels have been monitored for many years. This review summarises PCB trends in human milk across Europe. The trend is generally downward, with higher levels prevailing in urban areas near industrial plants. The highest PCB levels were reported in the Czech Republic and Slovakia.
DDC-Outlier: Preventing Medication Errors Using Unsupervised Learning

Electronic health records have brought valuable improvements to hospital practices by integrating patient information. In fact, the understanding of these data can prevent mistakes that may put patients’ lives at risk. Nonetheless, to the best of our knowledge, there are no previous studies addressing the automatic detection of outlier prescriptions, regarding dosage and frequency. In this paper, we propose an unsupervised method, called density-distance-centrality (DDC), to detect potential outlier prescriptions. A dataset with 563 thousand prescribed medications was used to assess our proposed approach against different state-of-the-art techniques for outlier detection. In the experiments, our approach achieves better results in the task of overdose and underdose detection in medical prescriptions, compared to other methods applied to this problem. Additionally, most of the false positive instances detected by our algorithm were potential prescriptions errors.
Diabetes Mellitus and Autoimmune Hepatitis: Demographical and Clinical Description of a Relatively Rare Phenotype

Abstract We studied demographic, metabolic, and clinical characteristics of patients with diabetes and autoimmune hepatitis (AIH) from the German/Austrian DPV registry. A total of 139 patients with diabetes and AIH were analyzed and compared to 437 728 patients with diabetes without AIH. The prevalence of AIH in patients with T1DM (44.8/100 000) seems higher than in the general population, the prevalence of AIH in patients with T2DM (23.6/100 000) does not seem to be increased. Patients with T2DM and AIH had a shorter duration of diabetes (p=0.007) and a higher proportion of females (p<0.001) compared to T2DM without AIH. Patients with diabetes (T1DM or T2DM) and AIH required higher insulin doses (p<0.001 and p=0.03, respectively) and showed increased liver enzymes (aspartate transaminase, alanine transaminase, gamma-glutamyltransferase) compared to diabetes patients without (all p<0.001). We detected a lower percentage of patients treated with oral antidiabetic drugs (p=0.01) and a higher percentage of patients treated by insulin in patients with T2DM and AIH (p<0.001) compared to patients with T2DM alone. We observed a higher incidence of autoimmune thyroid disease (AIT) in patients with diabetes (T1DM or T2DM) and AIH (p<0.001) compared to diabetes patients without AIH. AIH seems more frequent in patients with T1DM. Patients with diabetes and AIH require intensification of antidiabetic therapy and seem to have a higher prevalence of AIT.
Simultaneous Spectral Data Embedding and Clustering

Spectral clustering is often carried out by combining spectral data embedding and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means clustering. However, the aims, dimensionality reduction and clustering, are usually not performed jointly. In this brief, we propose a novel approach to finding an optimal spectral embedding for identifying a partition of the set of objects; it iteratively alternates spectral embedding and clustering. In doing so, we show that our model can learn a low-dimensional representation more suited to clustering. Compared with classical spectral clustering methods, the proposed algorithm is not costly and outperforms not only these methods but also other <italic>nonnegative matrix factorization</italic> variants.
Gender Differences in Medication Adherence Among Adolescent and Young Adult Kidney Transplant Recipients

Background. Among kidney transplant recipients, gender differences in medication adherence may contribute to higher graft failure risks observed in girls and young women compared with boys and young men. Our aim was to determine whether adherence differs by gender, and whether gender differences vary by age in adolescent and young adult kidney transplant recipients. Methods. We examined data from the 3-month run-in period (no intervention) of the randomized Teen Adherence in Kidney transplant Effectiveness of Intervention trial. Adherence was monitored using electronic pillboxes in 136 patients (11–24 y) followed in 8 transplant centers in Canada and the United States. We used ordinal logistic regression with generalized estimating equations to estimate the association between gender and each of daily taking (proportion of prescribed doses taken) and timing (proportion of prescribed doses taken on time) adherence, considering effect modification by age (11–16 y vs 17–24 y). Results. No difference in taking adherence was observed by gender among participants aged 11 to 16 years (odds ratio [OR], 0.92; 95% confidence interval [CI], 0.55–1.54), whereas among participants aged 17 to 24 years, women had significantly greater odds of higher taking adherence scores (OR, 3.03; 95% CI, 1.20–7.66) than men. Results were similar for timing adherence, with no difference among participants aged 11 to 16 years (OR, 1.03; 95% CI, 0.65–1.63) but a greater odds of higher timing adherence scores in women than in men among participants aged 17 to 24 years (OR, 3.26; 95% CI, 1.43–7.45). There were no differences in adherence assessed by self-report or SD of tacrolimus trough levels. Conclusions. Gender differences in adherence vary by age. Whereas younger adolescents show no adherence differences by gender, young women show much better adherence than young men.
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. We developed a method to automatically detect mitotic figures in breast cancer tissue sections based on convolutional neural networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by noisy and expensive reference standards established by pathologists, lack of generalization due to staining variation across laboratories, and high computational requirements needed to process gigapixel whole-slide images (WSIs). In this paper, we present a method to train and evaluate CNNs to specifically solve these issues in the context of mitosis detection in breast cancer WSIs. First, by combining image analysis of mitotic activity in phosphohistone-H3 restained slides and registration, we built a reference standard for mitosis detection in entire H&E WSIs requiring minimal manual annotation effort. Second, we designed a data augmentation strategy that creates diverse and realistic H&E stain variations by modifying H&E color channels directly. Using it during training combined with network ensembling resulted in a stain invariant mitosis detector. Third, we applied knowledge distillation to reduce the computational requirements of the mitosis detection ensemble with a negligible loss of performance. The system was trained in a single-center cohort and evaluated in an independent multicenter cohort from the cancer genome atlas on the three tasks of the tumor proliferation assessment challenge. We obtained a performance within the top three best methods for most of the tasks of the challenge.
Energy-Efficient Multicast Service Delivery Exploiting Single Frequency Device-To-Device Communications in 5G New Radio Systems

The forthcoming fifth generation (5G) networks are claimed to deliver the large amount of traffic generated by the huge number of heterogeneous devices that constitute the Internet of Things (IoT). This unprecedented volume of both human- and machine-generated traffic to be managed imposes 5G network operators to move the focus from throughput-optimized to energy-efficiency-optimized resource allocation solutions. Device-to-device (D2D) communications are recognized as an effective offloading technique that the 5G network can exploit to boost the capacity and energy efficiency of future 5G networks. In this paper, we design a technique to efficiently deliver multicast traffic in a 5G New Radio (NR) network by exploiting the benefits of D2D communication and single-frequency operation in order to improve the overall network energy efficiency. In the designed solution, the subset of devices in better channel conditions are served through a conventional multicast transmission, while cell-edge devices receive the multicast service from relay nodes that simultaneously transmit in D2D mode the same content. The dimension of the multicast serving area and the set of D2D connections to establish are chosen in order to maximize the overall network energy efficiency. Performed simulation results show the effectiveness of the proposed solution under varying frame configurations and number of multicast devices.
Adaptive Fault Estimation for T–S Fuzzy Interconnected Systems Based on Persistent Excitation Condition via Reference Signals

This paper is concerned with the fault estimation (FE) problem for a class of interconnected nonlinear systems described by Takagi–Sugeno fuzzy models. Different from the existing FE approaches, where the reference inputs are always viewed as external disturbances, the considered reference signals are injected directly in the controller to excite the system states to ensure persistent excitation condition. Within this framework, a bank of distributed adaptive observers are then designed to effectively estimate the actuator fault parameters even in the presence of nonlinear interconnections. Moreover, in the disturbance-free case, by employing graph theory, a global Lyapunov function is constructed such that the corresponding fault parameter estimate errors are proved to be asymptotically convergent provided that the interconnected strengths are less than a given constant. Finally, two simulation examples are provided to demonstrate the validity of the presented FE scheme.
Role of circuit representation in evolutionary design of energy-efficient approximate circuits

Circuit approximation has been introduced in recent years as a viable method for constructing energy-efficient electronic systems. An open problem is how to effectively obtain approximate circuits showing good compromises between key circuit parameters - the error, power consumption, area and delay. The use of evolutionary algorithms in the task of circuit approximation has led to promising results. Unfortunately, only relatively small circuit instances have been tackled because of the scalability problems of the evolutionary design method. This study demonstrates how to push the limits of the evolutionary design by choosing a more suitable representation on the one hand and a more efficient fitness function on the other hand. In particular, the authors show that employing full adders as building blocks leads to more efficient approximate circuits. The authors focused on the approximation of key arithmetic circuits such as adders and multipliers. While the evolutionary design of adders represents a rather easy benchmark problem, the design of multipliers is known to be one of the hardest problems. The authors evolved a comprehensive library of energy-efficient 12-bit multipliers with a guaranteed worst-case error. The library consists of 65 Pareto dominant solutions considering power, delay, area and error as design objectives.
Clarifying the Associations between Mindfulness Meditation and Emotion: Daily High- and Low-arousal Emotions and Emotional Variability.

BACKGROUND Research examining the effects of mindfulness meditation (MM) on emotion seldom considers differences by arousal level or emotion variability. METHODS In the present study, 115 participants (64% Female, 72% White, Mage = 19.03) were randomly assigned to a brief MM intervention condition (n = 60) or a wait-list control condition (n = 51). Participants in the MM condition were trained in MM and instructed to practice MM daily for one week. All participants provided daily diary reports of both higher- and lower-arousal positive (PE) and negative (NE) emotions. Emotions were weighted by valence and arousal. Multilevel modeling was used to examine valence, arousal, and their interaction; multivariate regression was used to examine emotional variability. RESULTS More time spent meditating (but not the MM condition itself) was associated with increased lower arousal emotions, and exhibited a significant effect on the interaction between valence and arousal. Examination of individual emotion items suggested that more time meditating significantly predicted increased feelings of quiet and calm and marginally increased relaxation and sleepiness among participants, but did not predict any other emotions assessed in daily life. MM was not associated with emotional variability. CONCLUSION These results may suggest that PE should be separated by arousal when examining the effects of MM interventions.
Association of Serum Leptin with All-Cause and Disease Specific Mortality: A Meta-Analysis of Prospective Observational Studies

Abstract Leptin levels may affect mortality through its link to inflammation and obesity. However, data are inconclusive. This meta-analysis was performed to investigate the association of leptin with mortality. The PubMed, Scopus, Google scholar, and reference lists of the included studies were searched for all published prospective observational studies that described the associations of serum leptin and mortality up to August 2017. Two reviewers independently assessed all potentially relevant studies for inclusion and methodological quality using standardized abstraction forms. Of 518 studies identified, 19 studies considered the association of leptin with all-cause mortality (16 208 subjects) and 12 studies evaluated the association of leptin with disease-specific mortality (13 680 subjects). In the overall analysis, no significant association was found between leptin and all-cause mortality (HR=1.028; 95% CI: 0.908–1.165; p=0.659), with a high between study heterogeneity (p˂0.001, I2=61). Subgroup analysis found that high leptin is associated with increased all-cause mortality in healthy people (HR=1.159 (95% CI, 1.032, 1.302), p=0.012), in men (HR=1.162; 95% CI: 1.036–1.302; p=0.010), and in subjects with ≥60 years old (HR=1.129; 95% CI: 1.030–1.238; p=0.010); whereas, increased leptin levels was related to the decreased cancer-specific mortality ((HR=0.550; 95% CI: 0.418–0.724), p˂0.001). Higher leptin levels in healthy people, old people, and males were associated with increased all-cause mortality, while increased leptin level was related to the reduced risk of death among patients with cancer.
Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises

In recent decades, primal-dual neural networks, as a special type of recurrent neural networks, have received great success in real-time manipulator control. However, noises are usually ignored when neural controllers are designed based on them, and thus, they may fail to perform well in the presence of intensive noises. Harmonic noises widely exist in real applications and can severely affect the control accuracy. This work proposes a novel primal-dual neural network design that directly takes noise control into account. By taking advantage of the fact that the unknown amplitude and phase information of a harmonic signal can be eliminated from its dynamics, our deliberately designed neural controller is able to reach the accurate tracking of reference trajectories in a noisy environment. Theoretical analysis and extensive simulations show that the proposed controller stabilizes the control system polluted by harmonic noises and converges the position tracking error to zero. Comparisons show that our proposed solution consistently and significantly outperforms the existing primal-dual neural solutions as well as feedforward neural one and adaptive neural one for redundancy resolution of manipulators.
Robust Gene Circuit Control Design for Time-Delayed Genetic Regulatory Networks Without SUM Regulatory Logic

This paper investigates the gene circuit control design problem of time-delayed genetic regulatory networks. In the genetic regulatory networks, the time delays are unknown constants, and the genetic regulatory is not conventional SUM regulatory logic and can be modeled to be an unknown nonlinear function of the time-delayed states of the other genes in a cell. By Lyapunov stability, a novel adaptive gene circuit control design approach is proposed for the genetic regulatory networks, where the unknown time delays are estimated online by adaptive algorithms and the unknown regulatory functions are approximated by neural networks. The design approach in this paper is delay-dependent and has less conservatism than the delay-independent approach. From theoretical analysis, the closed-loop system is asymptotically stable and all the signals in the system converge to an adjustable neighborhood of the origin. Finally, a numerical example is given to show the effectiveness of the new design approach.