MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations.

Drug combination therapies are common practice in the treatment of cancer, but not all combinations result in synergy. As traditional screening approaches are restricted in their ability to uncover synergistic drug combinations, computer-aided medicine is becoming a increasingly prevalent in this field. In this work, a predictive model of potential interactions between drugs named MPFFPSDC is presented, which can maintain the symmetry of drug inputs and eliminate inconsistencies in predictive results caused by different drug inputting sequences or positions. The experimental results show that MPFFPSDC outperforms comparative models in major performance indicators and exhibits better generalization for independent data. Furthermore, the case study demonstrates that our model can capture molecular substructures that contribute to the synergistic effect of two drugs. These results indicate that MPFFPSDC not only offers strong predictive performance, but also has good model interpretability that may provide new insights for the study of drug interaction mechanisms and the development of new drugs.

Juvenile hormone suppresses aggregation behavior through influencing antennal gene expression in locusts.

Animals often exhibit dramatically behavioral plasticity depending on their internal physiological state, yet little is known about the underlying molecular mechanisms. The migratory locust, Locusta migratoria, provides an excellent model for addressing these questions because of their famous phase polyphenism involving remarkably behavioral plasticity between gregarious and solitarious phases. Here, we report that a major insect hormone, juvenile hormone, is involved in the regulation of this behavioral plasticity related to phase change by influencing the expression levels of olfactory-related genes in the migratory locust. We found that the treatment of juvenile hormone analog, methoprene, can significantly shift the olfactory responses of gregarious nymphs from attraction to repulsion to the volatiles released by gregarious nymphs. In contrast, the repulsion behavior of solitarious nymphs significantly decreased when they were treated with precocene or injected with double-stranded RNA of JHAMT, a juvenile hormone acid O-methyltransferase. Further, JH receptor Met or JH-response gene Kr-h1 knockdown phenocopied the JH-deprivation effects on olfactory behavior. RNA-seq analysis identified 122 differentially expressed genes in antennae after methoprene application on gregarious nymphs. Interestingly, several olfactory-related genes were especially enriched, including takeout (TO) and chemosensory protein (CSP) which have key roles in behavioral phase change of locusts. Furthermore, methoprene application and Met or Kr-h1 knockdown resulted in simultaneous changes of both TO1 and CSP3 expression to reverse pattern, which mediated the transition between repulsion and attraction responses to gregarious volatiles. Our results suggest the regulatory roles of a pleiotropic hormone in locust behavioral plasticity through modulating gene expression in the peripheral olfactory system.

Global Mittag-Leffler synchronization of coupled delayed fractional reaction-diffusion Cohen-Grossberg neural networks via sliding mode control.

This paper studies the sliding mode control method for coupled delayed fractional reaction-diffusion Cohen-Grossberg neural networks on a directed non-strongly connected topology. A novel fractional integral sliding mode surface and the corresponding control law are designed to realize global Mittag-Leffler synchronization. The sufficient conditions for synchronization and reachability of the sliding mode surface are derived via the hierarchical method and the Lyapunov method. Finally, simulations are provided to verify our theoretical findings.

Dynamic Analysis and Optimal Control of Rumor Spreading Model with Recurrence and Individual Behaviors in Heterogeneous Networks.

This paper is devoted to investigating the impact of the recurrence of rumors and individual behaviors and control strategies related to rumor spreading in online social networks. To do this, a novel susceptible-hesitating-infected-latent-recovered (SHILR) rumor propagation model in heterogeneous networks is presented. Firstly, based on the relevant mean-field equations of the model, the threshold value is examined to demonstrate the existence and stability of rumor-free/spreading equilibrium with the help of the algebraic method. Secondly, the global stabilities of the equilibria are analyzed through applying Lyapunov stability theory and LaSalle's invariance principle. Next, the optimal control is proposed by taking advantage of Pontryagin's maximum principle for reducing the number of infected individuals with minimum cost. Moreover, some numerical examples are carried out to test the theoretical results. Finally, combined with practice, a model application is presented.

Synchronization in Coupled Neural Networks With Hybrid Delayed Impulses: Average Impulsive Delay-Gain Method.

In this article, we propose a new concept called average impulsive delay-gain (AIDG) for studying the synchronization of coupled neural networks (CNNs). Based on the viewpoints of impulsive control and impulsive perturbation, we establish some globally exponential synchronization criteria for CNNs. Our methods are well-suited for addressing the synchronization problems of systems subject to hybrid delayed impulses with time-varying impulsive delay and gain. Moreover, we prove that the AIDG has both positive and negative effects on synchronization. Compared to existing research, our conclusions are more applicable and less conservative as the considered hybrid delayed impulses involve more flexible cases. Finally, we validate the effectiveness of our proposed results by applying them to small-world and scale-free network models.

Practical Fixed-Time Bipartite Synchronization of Uncertain Coupled Neural Networks Subject to Deception Attacks via Dual-Channel Event-Triggered Control.

This article investigates the practical fixed-time synchronization of uncertain coupled neural networks via dual-channel event-triggered control. Contrary to some previous studies, the bipartite synchronization of signed graphs representing cooperative and antagonistic interactions is studied. The communication channel is introduced into deception attacks, which are described by Bernoulli's stochastic variables. Based on the concept of two channels, event-triggered mechanisms are designed for sensor-to-controller and controller-to-actuator channels to reduce communication consumption and controller update consumption as much as possible. Lyapunov and comparison theories are used to derive synchronization criteria and explicit expression of settling time. An example of Chua's circuit system is presented to demonstrate the feasibility of the obtained theoretical results.

Finite-Time and Fixed-Time Synchronization of Delayed Memristive Neural Networks via Adaptive Aperiodically Intermittent Adjustment Strategy.

This article investigates the finite-time and fixed-time synchronization for memristive neural networks (MNNs) with mixed time-varying delays under the adaptive aperiodically intermittent adjustment strategy. Different from previous works, this article first employs the aperiodically intermittent adjustment feedback control and adaptive control to drive the MNNs to achieve synchronization in finite time and fixed time. First of all, according to the theories of set-valued mappings and differential inclusions, the error MNNs is derived, and its finite-time and fixed-time stability problems are discussed by applying the Lyapunov function method and some LMI techniques. Moreover, by meticulously designing an effective aperiodically intermittent adjustment with adaptive updating law, sufficient conditions that guarantee the finite-time and fixed-time synchronization of the drive-response MNNs are obtained, and the settling time is explicitly estimated. Finally, three numerical examples are provided to illustrate the validity of the obtained theoretical results.

Output Tracking Control of Single-Input-Multioutput Systems Over an Erasure Channel.

The output tracking control problem is investigated in this article. First, a new tradeoff performance index is presented for single-input-multioutput (SIMO) systems. Based on the frequency-domain method, the tracking performance limitations under time delay, packet loss, and channel noise effects are derived. We use a bivariate stochastic process to model the packet loss, and assume that channel noise is additive white Gaussian noise (AWGN). Two explicit expressions of the best tradeoff performance are given with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structures. It is shown that the tracking control performance has a close relation with the intrinsic characteristic of the plant, as well as the time delay, packet-dropouts rate, and power spectral density of AWGN. We also demonstrate that compared with the SDOF control structure, the TDOF control structure can improve the systems' attainable performance. A simulation example is finally discussed to validate the conclusions.

Output Tracking Control Performance of Discrete Networked Systems Over Erasure Channel With Model Uncertainty.

For networked control systems, it is known that various communication parameters in the channel will pose some fundamental limitations on output tracking control (OTC) performance. In this study, we mainly discuss the limitations resulting from model uncertainties, involving channel and plant. Through using the bivariate stochastic process to model packet loss, and the assumption that channel noise is additive white Gaussian noise (AWGN), two explicit expressions of output tracking performance limitations are derived with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structure, which shows that the performance of OTC is closely related to the inherent characteristics of the plant, as well as the packet loss rate and power spectral density (PSD) of AWGN. Finally, by considering an illustrative example, the simulation results are verified and analyzed to ensure the effectiveness of treatment methods and results.

Synchronization criteria of delayed inertial neural networks with generally Markovian jumping.

In this paper, the synchronization problem of inertial neural networks with time-varying delays and generally Markovian jumping is investigated. The second order differential equations are transformed into the first-order differential equations by utilizing the variable transformation method. The Markovian process in the systems is uncertain or partially known due to the delay of data transmission channel or the loss of data information, which is more general and practicable to consider generally Markovian jumping inertial neural networks. The synchronization criteria can be obtained by using the delay-dependent Lyapunov-Krasovskii functionals and higher order polynomial based relaxed inequality (HOPRII). In addition, the desired controllers are obtained by solving a set of linear matrix inequalities. Finally, the numerical examples are provided to demonstrate the effectiveness of the theoretical results.

Mittag-Leffler Synchronization of Delayed Fractional Memristor Neural Networks via Adaptive Control.

This brief is devoted to exploring the global Mittag-Leffler (ML) synchronization problem of fractional-order memristor neural networks (FOMNNs) with leakage delay via a hybrid adaptive controller. By applying Fillipov's theory and the Lyapunov functional method, the novel algebraic sufficient condition for the global ML synchronization of FOMNNs is derived. Finally, a simulation example is presented to show the practicability of our findings.

Direct Adaptive Preassigned Finite-Time Control With Time-Delay and Quantized Input Using Neural Network.

This paper investigates an adaptive finite-time control (FTC) problem for a class of strict-feedback nonlinear systems with both time-delays and quantized input from a new point of view. First, a new concept, called preassigned finite-time performance function (PFTF), is defined. Then, another novel notion, called practically preassigned finite-time stability (PPFTS), is introduced. With PFTF and PPFTS in hand, a novel sufficient condition of the FTC is given by using the neural network (NN) control and direct adaptive backstepping technique, which is different from the existing results. In addition, a modified barrier function is first introduced in this work. Moreover, this work is first to focus on the FTC for the situation that the time-delay and quantized input simultaneously exist in the nonlinear systems. Finally, simulation results are carried out to illustrate the effectiveness of the proposed scheme.

SWE combined with ACR TI-RADS categories for malignancy risk stratification of thyroid nodules with indeterminate FNA cytology.

OBJECTIVES: To compare the diagnostic efficacy of shear wave elastography (SWE) comnined with ACR TI-RADS categories for malignancy risk stratification of thyroid nodules with interminate FNA cytology. METHODS: The clinical data, sonographic features, ACR TI-RADS grading and shear wave elastography images of 193 patients of surgical pathologically proven thyroid nodules with interminate FNA cytology were retrospectively analyzed. The diagnostic efficacy of ACR TI-RADS categories, the maximum Young's modulus (Emax) of SWE and the combination of the two were calculated respectively. RESULTS: The ROC curves were drawn using surgical pathology results as the gold standard. The ROC curves indicated that the cut-off value of ACR TI-RADS and Emax of SWE was TR5 and 41.2 kPa respectively, and the area under the ROC curve (AUC) was 0.864 (95% CI: 0.879-0.934) and 0.858 (95% CI: 0.796-0.920) respectively. The diagnostic sensitivity, specificity and accuracy of ACR TI-RADS was 81.4% (127/156), 84.8% (31/37), and 81.9% (158/193), respectively. That of SWE Emax was 80.8% (126/156), 78.4% (29/37), and 80.3% (155/193), respectively. After SWE combined with ACR TI-RADS, the sensitivity, specificity and accuracy was 94.2% (147/156), 75.7% (28/37), and 90.7% (175/193), respectively. CONCLUSIONS: ACR TI-RADS classification system and shear wave elastography had high diagnostic efficacy for thyroid nodules with interminate FNA cytology. The combination of the two could improve diagnostic sensitivity and accuracy, and could help to differentiate benign and malignant thyroid nodules with interminate FNA cytology.

Congestion tracking control for uncertain TCP/AQM network based on integral backstepping.

An H∞ congestion tracking control problem for nonlinear transmission control protocol/active queue management (TCP/AQM) network is proposed in this paper. Inspired by the existing network models, a modified TCP/AQM network model with external disturbance and modelling uncertainty is first introduced. And then, a novel function for the desired queue length is defined for the first time, which can simulate the changes in the queue length during a day. Next, a novel AQM scheme is presented to control network congestion by combining H∞ theory and integral backstepping technique. The proposed method guarantees that the better tracking performance of the queue is achieved, and all the signals are asymptotically stable in the closed-loop system in probability. Finally, the effectiveness of the presented algorithm is tested by simulation results.

Composition and emission dynamics of migratory locust volatiles in response to changes in developmental stages and population density.

Chemical communication plays an important role in density-dependent phase change in locusts. However, the volatile components and emission patterns of the migratory locust, Locusta migratoria, are largely unknown. In this study, we identified the chemical compositions and emission dynamics of locust volatiles from the body and feces and associated them with developmental stages, sexes and phase changes. The migratory locust shares a number of volatile components with the desert locust (Schistocerca gregaria), but the emission dynamics of the two locust species are significantly different. The body odors of the gregarious nymphs in the migratory locust consisted of phenylacetonitrile (PAN), benzaldehyde, guaiacol, phenol, aliphatic acids and 2,3-butanediol, and PAN was the dominant volatile. Volatiles from the fecal pellets of the nymphs primarily consist of guaiacol and phenol. Principal component analysis (PCA) showed significant differences in the volatile profiles between gregarious and solitary locusts. PAN and 4-vinylanisole concentrations were significantly higher in gregarious individuals than in solitary locusts. Gregarious mature males released significantly higher amounts of PAN and 4-vinylanisole during adulthood than mature females and immature adults of both sexes. Furthermore, PAN and 4-vinylanisole were completely lost in gregarious nymphs during the solitarization process, but were obtained by solitary nymphs during gregarization. The amounts of benzaldehyde, guaiacol and phenol only unidirectionally decreased from solitary to crowded treatment. Aliphatic aldehydes (C7 to C10), which were previously reported as locust volatiles, are now identified as environmental contaminants. Therefore, our results illustrate the precise odor profiles of migratory locusts during developmental stages, sexes and phase change. However, the function and role of PAN and other aromatic compounds during phase transition need further investigation.

Dynamic analysis of periodic solution for high-order discrete-time Cohen-Grossberg neural networks with time delays.

In this paper, we analyze the dynamic behavior of periodic solution for the high-order discrete-time Cohen-Grossberg neural networks (CGNNs) with time delays. First, the existence is studied based on the continuation theorem of coincidence degree theory and Young's inequality. And then, the criterion for the global exponential stability is given using Lyapunov method. Finally, simulation result shows the effectiveness of our proposed criterion.

The analysis of large-scale gene expression correlated to the phase changes of the migratory locust.

The migratory locust is one of the most notorious agricultural pests that undergo a well known reversible, density-dependent phase transition from the solitary to the gregarious. To demonstrate the underlying molecular mechanisms of the phase change, we generated 76,012 ESTs from the whole body and dissected organs in the two phases. Comparing 12,161 unigene clusters, we identified 532 genes as phase-related (P < 0.01). Comprehensive assessment of the phase-related expression revealed that, whereas most of the genes in various categories from hind legs and the midgut are down-regulated in the gregarious phase, several gene classes in the head are impressively up-regulated, including those with peptidase, receptor, and oxygen-binding activities and those related to development, cell growth, and responses to external stimuli. Among them, a superfamily of proteins, the JHPH super-family, which includes juvenile hormone-binding protein, hexamerins, prophenoloxidase, and hemocyanins, were highly expressed in the heads of the gregarious hoppers and hind legs of the solitary hoppers. Quantitative PCR experiments confirmed in part the EST results. These differentially regulated genes have strong functional implications that numerous molecular activities are involved in phase plasticity. This study provides ample molecular markers and genomic information on hemimetabolous insects and insights into the genetic and molecular mechanisms of phase changes in locusts.