Excitatory and inhibitory synaptic connectivity to layer V fast-spiking interneurons in the freeze lesion model of cortical microgyria

A variety of major developmental cortical malformations are closely associated with clinically intractable epilepsy. Pathophysiological aspects of one such disorder, human polymicrogyria, can be modeled by making neocortical freeze lesions (FL) in neonatal rodents, resulting in the formation of microgyri. Previous studies showed enhanced excitatory and inhibitory synaptic transmission and connectivity in cortical layer V pyramidal neurons in the paramicrogyral cortex. In young adult transgenic mice that express green fluorescent protein (GFP) specifically in parvalbumin positive fast-spiking (FS) interneurons, we used laser scanning photostimulation (LSPS) of caged glutamate to map excitatory and inhibitory synaptic connectivity onto FS interneurons in layer V of paramicrogyral cortex in control and FL groups. The proportion of uncaging sites from which excitatory postsynaptic currents (EPSCs) could be evoked (hotspot ratio) increased slightly but significantly in FS cells of the FL vs. control cortex, while the mean amplitude of LSPS-evoked EPSCs at hotspots did not change. In contrast, the hotspot ratio of inhibitory postsynaptic currents (IPSCs) was significantly decreased in FS neurons of the FL cortex. These alterations in synaptic inputs onto FS interneurons may result in an enhanced inhibitory output. We conclude that alterations in synaptic connectivity to cortical layer V FS interneurons do not contribute to hyperexcitability of the FL model. Instead, the enhanced inhibitory output from these neurons may partially offset an earlier demonstrated increase in synaptic excitation of pyramidal cells and thereby maintain a relative balance between excitation and inhibition in the affected cortical circuitry

Heterostructure of ferromagnetic and ferroelectric materials with magneto-optic and electro-optic effects

A heterostructure of multiferroics or magnetoelectrics (ME) was disclosed. The film has both ferromagnetic and ferroelectric properties, as well as magneto-optic (MO) and electro-optic (EO) properties. Oxide buffer layers were employed to allow grown a cracking-free heterostructure a solution coating method

Optimal Space-Depth Trade-Off of CNOT Circuits in Quantum Logic Synthesis

Due to the decoherence of the state-of-the-art physical implementations of quantum computers, it is essential to parallelize the quantum circuits to reduce their depth. Two decades ago, Moore et al. demonstrated that additional qubits (or ancillae) could be used to design "shallow" parallel circuits for quantum operators. They proved that any $n$-qubit CNOT circuit could be parallelized to $O(\log n)$ depth, with $O(n^2)$ ancillae. However, the near-term quantum technologies can only support limited amount of qubits, making space-depth trade-off a fundamental research subject for quantum-circuit synthesis. In this work, we establish an asymptotically optimal space-depth trade-off for the design of CNOT circuits. We prove that for any $m\geq0$, any $n$-qubit CNOT circuit can be parallelized to $O\left(\max \left\{\log n, \frac{n^{2}}{(n+m)\log (n+m)}\right\} \right)$ depth, with $O(m)$ ancillae. We show that this bound is tight by a counting argument, and further show that even with arbitrary two-qubit quantum gates to approximate CNOT circuits, the depth lower bound still meets our construction, illustrating the robustness of our result. Our work improves upon two previous results, one by Moore et al. for $O(\log n)$-depth quantum synthesis, and one by Patel et al. for $m = 0$: for the former, we reduce the need of ancillae by a factor of $\log^2 n$ by showing that $m=O(n^2/\log^2 n)$ additional qubits suffice to build $O(\log n)$-depth, $O(n^2/\log n)$ size --- which is asymptotically optimal --- CNOT circuits; for the later, we reduce the depth by a factor of $n$ to the asymptotically optimal bound $O(n/\log n)$. Our results can be directly extended to stabilizer circuits using an earlier result by Aaronson et al. In addition, we provide relevant hardness evidences for synthesis optimization of CNOT circuits in term of both size and depth.Comment: 25 pages, 5 figures. Fixed several minor typos and a mistake about CNOT+Rz circui

Suppression of KRas-mutant cancer through the combined inhibition of KRAS with PLK1 and ROCK

No effective targeted therapies exist for cancers with somatic KRAS mutations. Here we develop a synthetic lethal chemical screen in isogenic KRAS-mutant and wild-type cells to identify clinical drug pairs. Our results show that dual inhibition of polo-like kinase 1 and RhoA/Rho kinase (ROCK) leads to the synergistic effects in KRAS-mutant cancers. Microarray analysis reveals that this combinatory inhibition significantly increases transcription and activity of cyclin-dependent kinase inhibitor p21(WAF1/CIP1), leading to specific G2/M phase blockade in KRAS-mutant cells. Overexpression of p21(WAF1/CIP1), either by cDNA transfection or clinical drugs, preferentially impairs the growth of KRAS-mutant cells, suggesting a druggable synthetic lethal interaction between KRAS and p21(WAF1/CIP1). Co-administration of BI-2536 and fasudil either in the LSL-KRAS(G12D) mouse model or in a patient tumour explant mouse model of KRAS-mutant lung cancer suppresses tumour growth and significantly prolongs mouse survival, suggesting a strong synergy in vivo and a potential avenue for therapeutic treatment of KRAS-mutant cancers

Review on enhanced oil recovery by nanofluids

The addition of nanoparticles into water based fluids (nanofluid) with or without other chemicals to Enhance Oil Recovery (EOR) has recently received intensive interest. Many papers have been published in this area and several EOR mechanisms have been proposed. The main EOR mechanisms include wettability alteration, reduction in InterFacial surface Tension (IFT), increase in the viscosity of aqueous solution, decrease in oil viscosity, and log-jamming. Some of these mechanisms may be associated with the change in disjoining pressure because of the addition of the nanoparticles. The experimental data and results reported by different researchers, however, are not all consistent and some even conflict with others. Many papers published in recent years have been reviewed and the associated experimental data have been analyzed in this paper in order to clarify the mechanisms of EOR by nanofluids. Wettability alteration may be one of the most accepted mechanisms for nanofluid EOR while reduction in IFT and other mechanisms have not been fully proven. The main reason for the inconsistency among the experimental data might be lack of control experiments in which the effect of nanoparticles on oil recovery would be singled out

Review on enhanced oil recovery by nanofluids

The addition of nanoparticles into water based fluids (nanofluid) with or without other chemicals to Enhance Oil Recovery (EOR) has recently received intensive interest. Many papers have been published in this area and several EOR mechanisms have been proposed. The main EOR mechanisms include wettability alteration, reduction in InterFacial surface Tension (IFT), increase in the viscosity of aqueous solution, decrease in oil viscosity, and log-jamming. Some of these mechanisms may be associated with the change in disjoining pressure because of the addition of the nanoparticles. The experimental data and results reported by different researchers, however, are not all consistent and some even conflict with others. Many papers published in recent years have been reviewed and the associated experimental data have been analyzed in this paper in order to clarify the mechanisms of EOR by nanofluids. Wettability alteration may be one of the most accepted mechanisms for nanofluid EOR while reduction in IFT and other mechanisms have not been fully proven. The main reason for the inconsistency among the experimental data might be lack of control experiments in which the effect of nanoparticles on oil recovery would be singled out

Reordering Features with Weights Fusion in Multiclass and Multiple-Kernel Speech Emotion Recognition

The selection of feature subset is a crucial aspect in speech emotion recognition problem. In this paper, a Reordering Features with Weights Fusion (RFWF) algorithm is proposed for selecting more effective and compact feature subset. The RFWF algorithm fuses the weights reflecting the relevance, complementarity, and redundancy between features and classes comprehensively and implements the reordering of features to construct feature subset with excellent emotional recognizability. A binary-tree structured multiple-kernel SVM classifier is adopted in emotion recognition. And different feature subsets are selected in different nodes of the classifier. The highest recognition accuracy of the five emotions in Berlin database is 90.549% with only 15 features selected by RFWF. The experimental results show the effectiveness of RFWF in building feature subset and the utilization of different feature subsets for specified emotions can improve the overall recognition performance