Metabolic profiling of aortic stenosis and hypertrophic cardiomyopathy identifies mechanistic contrasts in substrate utilization.

Aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM) are distinct disorders leading to left ventricular hypertrophy (LVH), but whether cardiac metabolism substantially differs between these in humans remains to be elucidated. We undertook an invasive (aortic root, coronary sinus) metabolic profiling in patients with severe AS and HCM in comparison with non-LVH controls to investigate cardiac fuel selection and metabolic remodeling. These patients were assessed under different physiological states (at rest, during stress induced by pacing). The identified changes in the metabolome were further validated by metabolomic and orthogonal transcriptomic analysis, in separately recruited patient cohorts. We identified a highly discriminant metabolomic signature in severe AS in all samples, regardless of sampling site, characterized by striking accumulation of long-chain acylcarnitines, intermediates of fatty acid transport across the inner mitochondrial membrane, and validated this in a separate cohort. Mechanistically, we identify a downregulation in the PPAR-α transcriptional network, including expression of genes regulating fatty acid oxidation (FAO). In silico modeling of ß-oxidation demonstrated that flux could be inhibited by both the accumulation of fatty acids as a substrate for mitochondria and the accumulation of medium-chain carnitines which induce competitive inhibition of the acyl-CoA dehydrogenases. We present a comprehensive analysis of changes in the metabolic pathways (transcriptome to metabolome) in severe AS, and its comparison to HCM. Our results demonstrate a progressive impairment of ß-oxidation from HCM to AS, particularly for FAO of long-chain fatty acids, and that the PPAR-α signaling network may be a specific metabolic therapeutic target in AS.

Chaos, periodic structures, and multistability: Complex dynamical behaviors of an eco-epidemiological model in parameter planes.

The environmental characteristics of a biological system are imbibed in some particular parameters of that system. Significant changes in any system parameter exert influence on the system dynamics as well as the persistence of interacting species. In this article, we explore the rich and tangled dynamics of an eco-epidemiological system by studying different parametric planes of the system. In the parameter planes, we find a variety of complex and subtle properties of the system, like the presence of a variety of intricate regular structures within irregular regimes, that cannot be found through a single parameter variation. Also, we find a new type of structure like an "eye" in a parametric plane. We notice the bistability between distinct pairs of attractors and also identify the coexistence of three periodic attractors. The most notable observation of this study is the coexistence of three periodic attractors and a chaotic attractor, which is a rare occurrence in biological systems. We also plot the basins for each set of coexisting attractors and see the existence of fractal basins in the system, which look like a "conch." The appearance of fractal basins in a system causes enormous complications in predicting the system's state in the long run. Variations in initial conditions and changes in parameters in parametric planes are key to managing the behavior of a system.

Bifurcation, chaos, multistability, and organized structures in a predator-prey model with vigilance.

There is not a single species that does not strive for survival. Every species has crafted specialized techniques to avoid possible dangers that mostly come from the side of their predators. Survival instincts in nature led prey populations to develop many anti-predator strategies. Vigilance is a well-observed effective antipredator strategy that influences predator-prey dynamics significantly. We consider a simple discrete-time predator-prey model assuming that vigilance affects the predation rate and the growth rate of the prey. We investigate the system dynamics by constructing isoperiodic and Lyapunov exponent diagrams with the simultaneous variation of the prey's growth rate and the strength of vigilance. We observe a series of different types of organized periodic structures with different kinds of period-adding phenomena. The usual period-bubbling phenomenon is shown near a shrimp-shaped periodic structure. We observe the presence of double and triple heterogeneous attractors. We also notice Wada basin boundaries in the system, which is quite rare in ecological systems. The complex dynamics of the system in biparameter space are explored through extensive numerical simulations.

Dynamics of a stage-structured predator-prey model: cost and benefit of fear-induced group defense.

In the predator-prey system, predators can affect the prey population (1) by direct killing and (2) by inducing predation fear, which ultimately force preys to adopt some anti-predator strategies. However, the anti-predator strategy is not the same for all individual preys of different life stages. Also, anti-predator behavior has both cost and benefit, but most of the mathematical models observed the dynamics by incorporating its cost only. In the present study, we formulate a predator-prey model dividing the prey population into two stages: juvenile and adult. We assume that adult preys are only adapting group defense as an anti-predator strategy when they are sensitive to predation. Group defense plays a positive role for adult prey by reducing their predation, but, on the negative side, it simultaneously decreases their reproductive potential. A parameter, anti-predator sensitivity is introduced to interlink both the benefit and cost of group defense. Our result shows that when adult preys are not showing anti-predator behavior, with an increase of maturation rate, the system exhibits a population cycle of abruptly increasing amplitude, which may drive all species of the system to extinction. Anti-predator sensitivity may exclude oscillation through homoclinic bifurcation and avert the prey population for any possible random extinction. Anti-predator sensitivity also decreases the predator population density and produces bistable dynamics. Higher values of anti-predator sensitivity may lead to the extinction of the predator population and benefit adult preys to persist with large population density. Below a threshold value of anti-predator sensitivity, it may possible to retain the predator population in the system by increasing the fear level of the predator. We also observe our fear-induced stage-structured model exhibits interesting and rich dynamical behaviors, various types of bistabilities in different bi-parameter planes. Finally, we discuss the potential impact of our findings.

Bifurcations, chaos, and multistability in a nonautonomous predator-prey model with fear.

Classical predator-prey models usually emphasize direct predation as the primary means of interaction between predators and prey. However, several field studies and experiments suggest that the mere presence of predators nearby can reduce prey density by forcing them to adopt costly defensive strategies. Adoption of such kind would cause a substantial change in prey demography. The present paper investigates a predator-prey model in which the predator's consumption rate (described by a functional response) is affected by both prey and predator densities. Perceived fear of predators leads to a drop in prey's birth rate. We also consider both constant and time-varying (seasonal) forms of prey's birth rate and investigate the model system's respective autonomous and nonautonomous implementations. Our analytical studies include finding conditions for the local stability of equilibrium points, the existence, direction of Hopf bifurcation, etc. Numerical illustrations include bifurcation diagrams assisted by phase portraits, construction of isospike and Lyapunov exponent diagrams in bi-parametric space that reveal the rich and complex dynamics embedded in the system. We observe different organized periodic structures within the chaotic regime, multistability between multiple pairs of coexisting attractors with intriguing basins of attractions. Our results show that even relatively slight changes in system parameters, perturbations, or environmental fluctuations may have drastic consequences on population oscillations. Our observations indicate that the fear effect alters the system dynamics significantly and drives an otherwise irregular system toward regularity.

Case report describing late balloon dilatation as a treatment for paravalvular leak with intuity elite rapid deployment valve.

Use of sutureless bioprostheses for aortic valve replacement has increased in recent years as compared to conventional prostheses, though with the potential issue of paravalvular leak, which requires close follow-up. We present this case report describing the successful treatment of paravalvular leak in a 65 year old man, who had NYHA class III symptoms post implantation of a 21 mm Intuity Elite rapid deployment bioprosthesis (Edwards Lifesciences, Irvine, CA). Diagnosis was established using TTE, TOE, and Cardiac MRI. Performing balloon dilatation using an Atlas Gold balloon (BARD Peripheral Vascular Inc., Tempe, AZ) treated the likely inadequate expansion of the subvalvular stent, leading to significant reduction in the paravalvular leak. At one month follow-up patient reported complete resolution of his symptoms. Successful percutaneous treatment of paravalvular leak following implantation of rapid deployment sutureless bioprosthesis provides a new treatment strategy for these patients; this strategy requires further validation.

Cooperation delay induced chaos in an ecological system.

In the present paper, we investigate the impact of time delay during cooperative hunting in a predator-prey model. We consider that cooperative predators do not aggregate in a group instantly, but individuals use different stages and strategies such as tactile, visual, vocal cues, or a suitable combination of these to communicate with each other. We observe that delay in hunting cooperation has stabilizing as well as destabilizing effects in the system. Also, for an increase in the strength of the delay, the system dynamics switch multiple times and eventually become chaotic. We see that depending on the threshold of time delay, the system may restore its original state or may go far away from its original state and unable to recollect its memory. Furthermore, we explore the dynamics of the system in different bi-parameter spaces and observe that for a particular range of other parameter values, the system dynamics switch multiple times with an increase of delay in all the planes. Different kinds of multistability behaviors, the coexistence of multiple attractors, and interesting changes in the basins of attraction of the system are also observed. We infer that depending on the initial population size and the strength of cooperation delay, the populations can exhibit stable coexistence, oscillating coexistence, or extinction of the predator species.

The interplay between metabolic alterations, diastolic strain rate and exercise capacity in mild heart failure with preserved ejection fraction: a cardiovascular magnetic resonance study.

BACKGROUND: Heart failure (HF) is characterized by altered myocardial substrate metabolism which can lead to myocardial triglyceride accumulation (steatosis) and lipotoxicity. However its role in mild HF with preserved ejection fraction (HFpEF) is uncertain. We measured myocardial triglyceride content (MTG) in HFpEF and assessed its relationships with diastolic function and exercise capacity. METHODS: Twenty seven HFpEF (clinical features of HF, left ventricular EF >50%, evidence of mild diastolic dysfunction and evidence of exercise limitation as assessed by cardiopulmonary exercise test) and 14 controls underwent 1H-cardiovascular magnetic resonance spectroscopy (1H-CMRS) to measure MTG (lipid/water, %), 31P-CMRS to measure myocardial energetics (phosphocreatine-to-adenosine triphosphate - PCr/ATP) and feature-tracking cardiovascular magnetic resonance (CMR) imaging for diastolic strain rate. RESULTS: When compared to controls, HFpEF had 2.3 fold higher in MTG (1.45 ± 0.25% vs. 0.64 ± 0.16%, p = 0.009) and reduced PCr/ATP (1.60 ± 0.09 vs. 2.00 ± 0.10, p = 0.005). HFpEF had significantly reduced diastolic strain rate and maximal oxygen consumption (VO2 max), which both correlated significantly with elevated MTG and reduced PCr/ATP. On multivariate analyses, MTG was independently associated with diastolic strain rate while diastolic strain rate was independently associated with VO2 max. CONCLUSIONS: Myocardial steatosis is pronounced in mild HFpEF, and is independently associated with impaired diastolic strain rate which is itself related to exercise capacity. Steatosis may adversely affect exercise capacity by indirect effect occurring via impairment in diastolic function. As such, myocardial triglyceride may become a potential therapeutic target to treat the increasing number of patients with HFpEF.

Effect of Selective Heart Rate Slowing in Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is associated with significant morbidity and mortality but is currently refractory to therapy. Despite limited evidence, heart rate reduction has been advocated, on the basis of physiological considerations, as a therapeutic strategy in HFpEF. We tested the hypothesis that heart rate reduction improves exercise capacity in HFpEF. METHODS AND RESULTS: We conducted a randomized, crossover study comparing selective heart rate reduction with the If blocker ivabradine at 7.5 mg twice daily versus placebo for 2 weeks each in 22 symptomatic patients with HFpEF who had objective evidence of exercise limitation (peak oxygen consumption at maximal exercise [o2 peak] <80% predicted for age and sex). The result was compared with 22 similarly treated matched asymptomatic hypertensive volunteers. The primary end point was the change in o2 peak. Secondary outcomes included tissue Doppler-derived E/e' at echocardiography, plasma brain natriuretic peptide, and quality-of-life scores. Ivabradine significantly reduced peak heart rate compared with placebo in the HFpEF (107 versus 129 bpm; P<0.0001) and hypertensive (127 versus 145 bpm; P=0.003) cohorts. Ivabradine compared with placebo significantly worsened the change in o2 peak in the HFpEF cohort (-2.1 versus 0.9 mL·kg(-1)·min(-1); P=0.003) and significantly reduced submaximal exercise capacity, as determined by the oxygen uptake efficiency slope. No significant effects on the secondary end points were discernable. CONCLUSION: Our observations bring into question the value of heart rate reduction with ivabradine for improving symptoms in a HFpEF population characterized by exercise limitation. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02354573.

Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimer's disease.

MOTIVATION: Alzheimer's disease (AD) is a dementia that gets worse with time resulting in loss of memory and cognitive functions. The life expectancy of AD patients following diagnosis is ∼7 years. In 2006, researchers estimated that 0.40% of the world population (range 0.17-0.89%) was afflicted by AD, and that the prevalence rate would be tripled by 2050. Usually, examination of brain tissues is required for definite diagnosis of AD. So, it is crucial to diagnose AD at an early stage via some alternative methods. As the brain controls many functions via releasing signalling proteins through blood, we analyse blood plasma proteins for diagnosis of AD. RESULTS: Here, we use a radial basis function (RBF) network for feature selection called feature selection RBF network for selection of plasma proteins that can help diagnosis of AD. We have identified a set of plasma proteins, smaller in size than previous study, with comparable prediction accuracy. We have also analysed mild cognitive impairment (MCI) samples with our selected proteins. We have used neural networks and support vector machines as classifiers. The principle component analysis, Sammmon projection and heat-map of the selected proteins have been used to demonstrate the proteins' discriminating power for diagnosis of AD. We have also found a set of plasma signalling proteins that can distinguish incipient AD from MCI at an early stage. Literature survey strongly supports the AD diagnosis capability of the selected plasma proteins.

Discovering monotonic stemness marker genes from time-series stem cell microarray data.

BACKGROUND: Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics. RESULTS: We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages. CONCLUSIONS: We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/.

Adenosine stress native T1 mapping in severe aortic stenosis: evidence for a role of the intravascular compartment on myocardial T1 values.

BACKGROUND: Myocardial T1 relaxation times have been reported to be markedly abnormal in diverse myocardial pathologies, ascribed to interstitial changes, evaluated by T1 mapping and calculation of extracellular volume (ECV). T1 mapping is sensitive to myocardial water content of both intra- and extracellular in origin, but the effect of intravascular compartment changes on T1 has been largely neglected. We aimed to assess the role of intravascular compartment on native (pre-contrast) T1 values by studying the effect of adenosine-induced vasodilatation in patients with severe aortic stenosis (AS) before and after aortic valve replacement (AVR). METHODS: 42 subjects (26 patients with severe AS without obstructive coronary artery disease and 16 controls) underwent cardiovascular magnetic resonance at 3 T for native T1-mapping (ShMOLLI), first-pass perfusion (myocardial perfusion reserve index-MPRI) at rest and during adenosine stress, and late gadolinium enhancement (LGE). RESULTS: AS patients had increased resting myocardial T1 (1196±47 ms vs. 1168±27 ms, p=0.037), reduced MPRI (0.92±0.31 vs. 1.74±0.32, p<0.001), and increased left ventricular mass index (LVMI) and LGE volume compared to controls. During adenosine stress, T1 in AS was similar to controls (1240±51 ms vs. 1238±54 ms, p=0.88), possibly reflecting a similar level of maximal coronary vasodilatation in both groups. Conversely, the T1 response to stress was blunted in AS (ΔT1 3.7±2.7% vs. 6.0±4.2% in controls, p=0.013). Seven months after AVR (n=16) myocardial T1 and response to adenosine stress recovered towards normal. Native T1 values correlated with reduced MPRI, aortic valve area, and increased LVMI. CONCLUSIONS: Our study suggests that native myocardial T1 values are not only influenced by interstitial and intracellular water changes, but also by changes in the intravascular compartment. Performing T1 mapping during or soon after vasodilator stress may affect ECV measurements given that hyperemia alone appears to substantially alter T1 values.

Myocardial perfusion and oxygenation are impaired during stress in severe aortic stenosis and correlate with impaired energetics and subclinical left ventricular dysfunction.

BACKGROUND: Left ventricular (LV) hypertrophy in aortic stenosis (AS) is characterized by reduced myocardial perfusion reserve due to coronary microvascular dysfunction. However, whether this hypoperfusion leads to tissue deoxygenation is unknown. We aimed to assess myocardial oxygenation in severe AS without obstructive coronary artery disease, and to investigate its association with myocardial energetics and function. METHODS: Twenty-eight patients with isolated severe AS and 15 controls underwent cardiovascular magnetic resonance (CMR) for assessment of perfusion (myocardial perfusion reserve index-MPRI) and oxygenation (blood-oxygen level dependent-BOLD signal intensity-SI change) during adenosine stress. LV circumferential strain and phosphocreatine/adenosine triphosphate (PCr/ATP) ratios were assessed using tagging CMR and 31P MR spectroscopy, respectively. RESULTS: AS patients had reduced MPRI (1.1 ± 0.3 vs. controls 1.7 ± 0.3, p < 0.001) and BOLD SI change during stress (5.1 ± 8.9% vs. controls 18.2 ± 10.1%, p = 0.001), as well as reduced PCr/ATP (1.45 ± 0.21 vs. 2.00 ± 0.25, p < 0.001) and LV strain (-16.4 ± 2.7% vs. controls -21.3 ± 1.9%, p < 0.001). Both perfusion reserve and oxygenation showed positive correlations with energetics and LV strain. Furthermore, impaired energetics correlated with reduced strain. Eight months post aortic valve replacement (AVR) (n = 14), perfusion (MPRI 1.6 ± 0.5), oxygenation (BOLD SI change 15.6 ± 7.0%), energetics (PCr/ATP 1.86 ± 0.48) and circumferential strain (-19.4 ± 2.5%) improved significantly. CONCLUSIONS: Severe AS is characterized by impaired perfusion reserve and oxygenation which are related to the degree of derangement in energetics and associated LV dysfunction. These changes are reversible on relief of pressure overload and hypertrophy regression. Strategies aimed at improving oxygen demand-supply balance to preserve myocardial energetics and LV function are promising future therapies.

Bi-Level Spectral Feature Selection.

Unsupervised feature selection (UFS) aims to learn an indicator matrix relying on some characteristics of the high-dimensional data to identify the features to be selected. However, traditional unsupervised methods perform only at the feature level, i.e., they directly select useful features by feature ranking. Such methods do not pay any attention to the interaction information with other tasks such as classification, which severely degrades their feature selection performance. In this article, we propose an UFS method which also takes into account the classification level, and selects features that perform well both in clustering and classification. To achieve this, we design a bi-level spectral feature selection (BLSFS) method, which combines classification level and feature level. More concretely, at the classification level, we first apply the spectral clustering to generate pseudolabels, and then train a linear classifier to obtain the optimal regression matrix. At the feature level, we select useful features via maintaining the intrinsic structure of data in the embedding space with the learned regression matrix from the classification level, which in turn guides classifier training. We utilize a balancing parameter to seamlessly bridge the classification and feature levels together to construct a unified framework. A series of experiments on 12 benchmark datasets are carried out to demonstrate the superiority of BLSFS in both clustering and classification performance.

Multimodal fusion for anticipating human decision performance.

Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response correctness in a demanding visual searching task. Notably, we extract a novel set of image features pertaining to object relationships using the Segment Anything Model (SAM), which enhances prediction accuracy compared to traditional features. Additionally, our approach effectively utilizes a combination of EEG signals and image features to streamline the feature set required for the Random Forest Classifier (RFC) while maintaining high accuracy. The findings of this research hold substantial potential for developing advanced fault alert systems, particularly in critical decision-making environments such as the medical and defence sectors.

Universal Approximation Abilities of a Modular Differentiable Neural Network.

Approximation ability is one of the most important topics in the field of neural networks (NNs). Feedforward NNs, activated by rectified linear units and some of their specific smoothed versions, provide universal approximators to convex as well as continuous functions. However, most of these networks are investigated empirically, or their characteristics are analyzed based on specific operation rules. Moreover, an adequate level of interpretability of the networks is missing as well. In this work, we propose a class of new network architecture, built with reusable neural modules (functional blocks), to supply differentiable and interpretable approximators for convex and continuous target functions. Specifically, first, we introduce a concrete model construction mechanism with particular blocks based on differentiable programming and the composition essence of the max operator, extending the scope of existing activation functions. Moreover, explicit block diagrams are provided for a clear understanding of the external architecture and the internal processing mechanism. Subsequently, the approximation behavior of the proposed network to convex functions and continuous functions is rigorously proved as well, by virtue of mathematical induction. Finally, plenty of numerical experiments are conducted on a wide variety of problems, which exhibit the effectiveness and the superiority of the proposed model over some existing ones.

Supervised Feature Selection via Collaborative Neurodynamic Optimization.

As a crucial part of machine learning and pattern recognition, feature selection aims at selecting a subset of the most informative features from the set of all available features. In this article, supervised feature selection is at first formulated as a mixed-integer optimization problem with an objective function of weighted feature redundancy and relevancy subject to a cardinality constraint on the number of selected features. It is equivalently reformulated as a bound-constrained mixed-integer optimization problem by augmenting the objective function with a penalty function for realizing the cardinality constraint. With additional bilinear and linear equality constraints for realizing the integrality constraints, it is further reformulated as a bound-constrained biconvex optimization problem with two more penalty terms. Two collaborative neurodynamic optimization (CNO) approaches are proposed for solving the formulated and reformulated feature selection problems. One of the proposed CNO approaches uses a population of discrete-time recurrent neural networks (RNNs), and the other use a pair of continuous-time projection networks operating concurrently on two timescales. Experimental results on 13 benchmark datasets are elaborated to substantiate the superiority of the CNO approaches to several mainstream methods in terms of average classification accuracy with three commonly used classifiers.

Fractional Approximation of Broad Learning System.

Approximation ability is of much importance for neural networks. The broad learning system (BLS) (Chen and Liu, 2018), widely used in the industry with good performance, has been proved to be a universal approximator from the aspect of density. This kind of approximation property is very important, which proves the existence of the desired network but does not provide a means of construction that is commonly implemented through complexity aspect. Thus, such an approach lacks the advantage of determining constructively the network architecture and its weights. To the best of our knowledge, for a BLS, there is a few theory providing a constructive approach to obtain the network structure along with weights ensuring the approximation properties. By virtue of the long-term memory and nonlocality properties, fractional calculus has observed many distinctive applications. The purpose of this article is to study the BLS approximation ability constructively, which is valid for fractional case as well. Specifically, first we introduce two simplified BLSs by means of extending functions. For each of the simplified BLSs, an upper bound of error is derived through the modulus of continuity of Caputo fractional derivatives. As a result, two types of fractional convergent behaviors of BLS, that is: 1) pointwise and 2) uniform convergence, have been rigorously proved as well. Finally, some numerical experiments are conducted to demonstrate the approximation capabilities of BLSs.

Unsupervised feature selection via adaptive autoencoder with redundancy control.

Unsupervised feature selection is one of the efficient approaches to reduce the dimension of unlabeled high-dimensional data. We present a novel adaptive autoencoder with redundancy control (AARC) as an unsupervised feature selector. By adding two Group Lasso penalties to the objective function, AARC integrates unsupervised feature selection and determination of a compact network structure into a single framework. Besides, a penalty based on a measure of dependency between features (such as Pearson correlation, mutual information) is added to the objective function for controlling the level of redundancy in the selected features. To realize the desired effects of different regularizers in different phases of the training, we introduce adaptive parameters which change with iterations. In addition, a smoothing function is utilized to approximate the three penalties since they are not differentiable at the origin. An ablation study is carried out to validate the capabilities of redundancy control and structure optimization of AARC. Subsequently, comparisons with nine state-of-the-art methods illustrate the efficiency of AARC for unsupervised feature selection.