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Glucose is a primary fuel source to the brain, yet the influence of dysglycemia on neurodevelopment in children with type 1 diabetes remains unclear. We examined brain activation using functional MRI in 80 children with type 1 diabetes (mean age ± SD, 11.5±1.8 years; 46% female) and 47 children without diabetes (“control”, mean age 11.8±1.5 years; 51% female) as they performed a visuospatial working memory (N-back) task. Results indicated that in both groups, activation scaled positively with increasing working memory load across many areas, including the frontoparietal cortex, caudate and cerebellum. Between groups, children with diabetes exhibited reduced performance on the N-back task relative to control children, as well as greater modulation of activation (i.e., showed greater a increase in activation with higher working memory load). Post-hoc analyses indicated that greater modulation was associated in the diabetes group with better working memory function and with an earlier age of diagnosis. These findings suggest that increased modulation may occur as a compensatory mechanism, helping in part to preserve working memory ability, and further, that children with an earlier onset require additional compensation. Future studies that test whether these patterns change as a function of improved glycemic control are warranted.Painful diabetic polyneuropathy (DPN) is difficult to manage as treatment response is often varied. The primary aim of this study was to examine differences in pain phenotypes between responders and non-responders to intravenous lidocaine treatment using quantitative sensory testing. The secondary aim was to explore differences in brain structure and functional connectivity with treatment response. Forty-five consecutive patients who received intravenous lidocaine treatment for painful DPN were screened. Twenty-nine patients who met the eligibility criteria (responders n=14 and non-responders n=15) and 26 healthy controls underwent detailed sensory profiling. Subjects also underwent multimodal brain magnetic resonance (MR) imaging. Greater proportion of patients with the irritable (IR) nociceptor phenotype were responders to intravenous lidocaine treatment compared to non-responders . The odd-ratio of responding to intravenous lidocaine was 8.67 times greater (95%CI 1.453.8) for the IR nociceptor phenotype. Responders to intravenous lidocaine also had significantly greater mean S1 cortical volume and functional connectivity between the insula cortex and the corticolimbic circuitry. Rapamycin This study provides preliminary evidence for a mechanism-based approach for individualising therapy in patients with painful DPN.This article investigates the problem of secure state estimation for continuous-time linear systems in the presence of sparse sensor attacks. Compared with the existing results, the attacked sensor set can be changed by adversaries against secure estimation. To address the more erratic attacks, a novel supervisory state observer is proposed, which employs a bank of candidate nonlinear subobservers and a switching logic administrated by a monitoring function to select the active subobserver at every instant of time. Based on the stability analysis of switched systems, it is proven that the supervisory observer asymptotically converges to a neighborhood of the true system state in the presence of the sensor attacks and bounded disturbances. A simulation example is given to substantiate the theoretical results.This article studies the containment control problem for a group of linear systems, consisting of more than one leader, over switching topologies. The input matrices of these linear systems are not required to have full-row rank and the switching can be arbitrary, making the problem quite general and challenging. We propose a novel analysis framework from the viewpoint of a state transition matrix. Specifically, according to the inherent linearity, we successfully establish a connection between state transition matrices of the above multileader system and a virtual leader-following system obtained by combining those leaders. This enlightening result relates the containment problem to a consensus one. Then, by analyzing the property of the state transition matrix, we uncover that each component of any follower’s state converges to the convex hull spanned by the corresponding components of the leaders’, provided some mild conditions are satisfied. These conditions are derived in terms of the concept of a positive linear system. A special case of the second-order linear system is further discussed to illustrate these conditions. Moreover, two different design methods of the feedback gain matrix are provided, which additionally require that the network topology contains a united spanning tree all the time.This article addresses the problem of global stabilization of continuous-time linear systems subject to control constraints using a model-free approach. We propose a gain-scheduled low-gain feedback scheme that prevents saturation from occurring and achieves global stabilization. The framework of parameterized algebraic Riccati equations (AREs) is employed to design the low-gain feedback control laws. An adaptive dynamic programming (ADP) method is presented to find the solution of the parameterized ARE without requiring the knowledge of the system dynamics. In particular, we present an iterative ADP algorithm that searches for an appropriate value of the low-gain parameter and iteratively solves the parameterized ADP Bellman equation. We present both state feedback and output feedback algorithms. The closed-loop stability and the convergence of the algorithm to the nominal solution of the parameterized ARE are shown. The simulation results validate the effectiveness of the proposed scheme.This article addresses the formation control problem of flapping-wing vehicles (FWVs) under the model uncertainty and the measurement inaccuracy. A two-layer formation strategy is adopted, which consists of a formation control layer for the leaders, and a containment control layer for the followers. In both layers, attitudes and positions are required by the formation geometry. A formation state estimation algorithm is designed to achieve the desired formation states from local neighborhoods. In FWVs, attitude angles are usually achieved from angular velocities, whose measurement error accumulates during integration and leads to divergence of the system. In order to solve this problem, we explore the coupling property between the translational motion and the rotational motion of FWVs, and design a coupling-based estimation method for attitude angles. To compensate for the model uncertainty, the measurement error, and the estimation error, adaptive neural networks are developed together with the control algorithm.