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γ-Aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the mature brain but has the paradoxical property of depolarizing neurons during early development. Depolarization provided by GABAA transmission during this early phase regulates neural stem cell proliferation, neural migration, neurite outgrowth, synapse formation, and circuit refinement, making GABA a key factor in neural circuit development. Importantly, depending on the context, depolarizing GABAA transmission can either drive neural activity or inhibit it through shunting inhibition. The varying roles of depolarizing GABAA transmission during development, and its ability to both drive and inhibit neural activity, makes it a difficult developmental cue to study. This is particularly true in the later stages of development when the majority of synapses form and GABAA transmission switches from depolarizing to hyperpolarizing. Here, we addressed the importance of depolarizing but inhibitory (or shunting) GABAA transmission in glutamaterg depolarizing GABAA transmission in shaping excitatory connectivity during neural circuit development. Copyright © 2020 Salmon, Pribiag, Gizowski, Farmer, Cameron, Jones, Mahadevan, Bourque, Stellwagen, Woodin and Murai.Selection of the time-window mainly affects the effectiveness of piecewise feature extraction procedures. We present an enhanced bag-of-patterns representation that allows capturing the higher-level structures of brain dynamics within a wide window range. So, we introduce augmented instance representations with extended window lengths for the short-time Common Spatial Pattern algorithm. Based on multiple-instance learning, the relevant bag-of-patterns are selected by a sparse regression to feed a bag classifier. The proposed higher-level structure representation promotes two contributions (i) accuracy improvement of bi-conditional tasks, (ii) A better understanding of dynamic brain behavior through the learned sparse regression fits. Pyrintegrin Using a support vector machine classifier, the achieved performance on a public motor imagery dataset (left-hand and right-hand tasks) shows that the proposed framework performs very competitive results, providing robustness to the time variation of electroencephalography recordings and favoring the class separability. Copyright © 2020 Collazos-Huertas, Caicedo-Acosta, Castaño-Duque and Acosta-Medina.An a posteriori estimate for the error of a standard Krylov approximation to the matrix exponential is derived. The estimate is based on the defect (residual) of the Krylov approximation and is proven to constitute a rigorous upper bound on the error, in contrast to existing asymptotical approximations. It can be computed economically in the underlying Krylov space. In view of time-stepping applications, assuming that the given matrix is scaled by a time step, it is shown that the bound is asymptotically correct (with an order related to the dimension of the Krylov space) for the time step tending to zero. This means that the deviation of the error estimate from the true error tends to zero faster than the error itself. Furthermore, this result is extended to Krylov approximations of φ -functions and to improved versions of such approximations. The accuracy of the derived bounds is demonstrated by examples and compared with different variants known from the literature, which are also investigated more closely. Alternative error bounds are tested on examples, in particular a version based on the concept of effective order. For the case where the matrix exponential is used in time integration algorithms, a step size selection strategy is proposed and illustrated by experiments. © The Author(s) 2019.Unlike performance incentives for private sector managers, little is known about performance incentives for managers in public sector bureaucracies. Through a randomized trial in rural China, we study performance incentives rewarding school administrators for reducing student anemia-as well as complementarity between incentives and orthogonally assigned discretionary resources. Large (but not small) incentives and unrestricted grants both reduced anemia, but incentives were more cost-effective. Although unrestricted grants and small incentives do not interact, grants fully crowd-out the effect of larger incentives. Our findings suggest that performance incentives can be effective in bureaucratic environments, but they are not complementary to discretionary resources. © The Author(s) 2019. Published by Oxford University Press on behalf of European Economic Association.Objectives To demonstrate safety and efficacy of using different generations of softer Amplatzer™ devices for ventricular septal defect (VSD) closure to avoid serious complications at follow-up. Background Transcatheter closure of perimembranous ventricular septal defects (PmVSD) is a well-established procedure; however, it is associated with unacceptable incidence of complete heart block. Great advantages have been achieved by using softer devices for VSD transcatheter closure. The first and second generation of Amplatzer™ occluders (AVP II, ADO, and ADO II) seem to offer a safe and attractive alternative for this procedure. These devices can be delivered using either an arterial (retrograde) or venous (prograde) approach. Methods and Results Patients with congenital PmVSD who underwent transcatheter closure using ADO, ADO II, and AVP II devices were included. Primary end point was to determine efficacy and safety of these generations of devices and to determine the incidence of complications at follow-up (complete AV block and aortic/tricuspid/mitral regurgitation). One hundred and nineteen patients underwent VSD closure at a median age of 5 years (8 months-54 years). During the catheterization, there were only minor complications and at follow-up of 36 ± 25.7 months (up to 99 months), the closure rate was high of 98.3% and freedom from AV block was 100%. Conclusions The use of softer Amplatzer™ devices is a good alternative to achieve PmVSD closure safely with no risk of AVB during the procedure or at midterm follow-up. Copyright © 2020 Roberto Mijangos-Vázquez et al.