Notch Signaling Activation Promotes Seizure Activity in Temporal Lobe Epilepsy

Notch signaling in the nervous system is often regarded as a developmental pathway. However, recent studies have suggested that Notch is associated with neuronal discharges. Here, focusing on temporal lobe epilepsy, we found that Notch signaling was activated in the kainic acid (KA)-induced epilepsy model and in human epileptogenic tissues. Using an acute model of seizures, we showed that DAPT, an inhibitor of Notch, inhibited ictal activity. In contrast, pretreatment with exogenous Jagged1 to elevate Notch signaling before KA application had proconvulsant effects. In vivo, we demonstrated that the impacts of activated Notch signaling on seizures can in part be attributed to the regulatory role of Notch signaling on excitatory synaptic activity in CA1 pyramidal neurons. In vitro, we found that DAPT treatment impaired synaptic vesicle endocytosis in cultured hippocampal neurons. Taken together, our findings suggest a correlation between aberrant Notch signaling and epileptic seizures. Notch signaling is up-regulated in response to seizure activity, and its activation further promotes neuronal excitation of CA1 pyramidal neurons in acute seizures

Aharonov-Anandan Effect Induced by Spin-Orbit Interaction and Charge-Density-Waves in Mesoscopic Rings

We study the spin-dependent geometric phase effect in mesoscopic rings of charge-density-wave(CDW) materials. When electron spin is explicitly taken into account, we show that the spin-dependent Aharonov-Casher phase can have a pronounced frustration effects on such CDW materials with appropriate electron filling. We show that this frustration has observable consequences for transport experiment. We identify a phase transition from a Peierls insulator to metal, which is induced by spin-dependent phase interference effects. Mesoscopic CDW materials and spin-dependent geometric phase effects, and their interplay, are becoming attractive opportunities for exploitation with the rapid development of modern fabrication technology.Comment: 5 pages, 6 figures, to appear in Phys.Rev.B(Aug.15, 1998

Early response to antidepressant treatment in bulimia nervosa

Background. Bulimia nervosa (BN) is a serious psychiatric disorder characterized by frequent episodes of binge eating and inappropriate compensatory behavior. Numerous trials have found that antidepressant medications are efficacious for the treatment of BN. Early response to antidepressant treatment, in the first few weeks after medication is initiated, may provide clinically useful information about an individual's likelihood of ultimately benefitting or not responding to such treatment. The purpose of this study was to examine the relationship between initial and later response to fluoxetine, the only antidepressant medication approved by the US Food and Drug Administration (FDA) for the treatment of BN, with the goal of developing guidelines to aid clinicians in deciding when to alter the course of treatment. Method. Data from the two largest medication trials conducted in BN (n=785) were used. Receiver operating characteristic (ROC) curves were constructed to assess whether symptom change during the first several weeks of treatment was associated with eventual non-response to fluoxetine at the end of the trial. Results. Eventual non-responders to fluoxetine could be reliably identified by the third week of treatment. Conclusions. Patients with BN who fail to report a o60 % decrease in the frequency of binge eating or vomiting at week 3 are unlikely to respond to fluoxetine. As no reliable relationships between pretreatment characteristics and eventual response to pharmacotherapy have been identified for BN, early response is one of the only available indicators to guide clinical management

Detection of rare variant effects in association studies: extreme values, iterative regression, and a hybrid approach

We develop statistical methods for detecting rare variants that are associated with quantitative traits. We propose two strategies and their combination for this purpose: the iterative regression strategy and the extreme values strategy. In the iterative regression strategy, we use iterative regression on residuals and a multimarker association test to identify a group of significant variants. In the extreme values strategy, we use individuals with extreme trait values to select candidate genes and then test only these candidate genes. These two strategies are integrated into a hybrid approach through a weighting technology. We apply the proposed methods to analyze the Genetic Analysis Workshop 17 data set. The results show that the hybrid approach is the most powerful approach. Using the hybrid approach, the average power to detect causal genes for Q1 is about 40% and the powers to detect FLT1 and KDR are 100% and 68% for Q1, respectively. The powers to detect VNN3 and BCHE are 34% and 30% for Q2, respectively

Flow and Solute Transport through a Single Fracture during the Biodegradation of Residual Toluene

A series of bench scale experiments were performed to assess the effects of biofilm and bio-enhanced toluene dissolution on flow and solute transport through a rough fracture. The fracture was cast from a real shale rock fracture. Sterilized artificial groundwater was used as the nutrition source to support the growth of microorganisms which were obtained from local groundwater. Hydraulic and tracer tests were carried out before and after the injection of toluene, and during the toluene biodegradation as well. The normalized hydraulic conductivity decreased sigmoidally during the experiment, and reached 0.02 at the end of the experiment. Biofilm growth was the main cause for hydraulic conductivity reduction after the injection of liquid toluene. The presence of separate phase toluene extended the tracer tailing compared to tracer tests without toluene. The longitudinal dispersion coefficient DL was proportional to the 1.5th power of the mean velocity in the rough fracture with or without residual toluene. Though a biofilm developed in the fracture during bio-treatment, the effect of secondary biofilm-associated porosity on solute transport in the fracture had negligible effect on tracer transport due to its small thickness. It is also found that DL decrease exponentially with Pe reduction during the bio-treatment

Quasiparticle Liquid in the Highly Overdoped Bi2212

We present results from the study of a highly overdoped (OD) Bi2212 with a $T_{c}=51$K using high resolution angle-resolved photoemission spectroscopy. The temperature dependent spectra near the ($\pi,0$) point show the presence of the sharp peak well above $T_{c}$. From the nodal direction, we make comparison of the self-energy with the optimally doped and underdoped cuprates, and the Mo(110) surface state. We show that this OD cuprate appears to have properties that approach that of the Mo. Further analysis shows that the OD has a more $k$-independent lineshape at the Fermi surface than the lower-doped cuprates. This allows for a realistic comparison of the nodal lifetime values to the experimental resistivity measurements via Boltzmann transport formulation. All these observations point to the validity of the quasiparticle picture for the OD even in the normal state within a certain energy and momentum range.Comment: 4 pages, 4 figure

The Incremental Cooperative Design of Preventive Healthcare Networks

This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe

LNCS

A controller is a device that interacts with a plant. At each time point,it reads the plant’s state and issues commands with the goal that the plant oper-ates optimally. Constructing optimal controllers is a fundamental and challengingproblem. Machine learning techniques have recently been successfully applied totrain controllers, yet they have limitations. Learned controllers are monolithic andhard to reason about. In particular, it is difficult to add features without retraining,to guarantee any level of performance, and to achieve acceptable performancewhen encountering untrained scenarios. These limitations can be addressed bydeploying quantitative run-timeshieldsthat serve as a proxy for the controller.At each time point, the shield reads the command issued by the controller andmay choose to alter it before passing it on to the plant. We show how optimalshields that interfere as little as possible while guaranteeing a desired level ofcontroller performance, can be generated systematically and automatically usingreactive synthesis. First, we abstract the plant by building a stochastic model.Second, we consider the learned controller to be a black box. Third, we mea-surecontroller performanceandshield interferenceby two quantitative run-timemeasures that are formally defined using weighted automata. Then, the problemof constructing a shield that guarantees maximal performance with minimal inter-ference is the problem of finding an optimal strategy in a stochastic2-player game“controller versus shield” played on the abstract state space of the plant with aquantitative objective obtained from combining the performance and interferencemeasures. We illustrate the effectiveness of our approach by automatically con-structing lightweight shields for learned traffic-light controllers in various roadnetworks. The shields we generate avoid liveness bugs, improve controller per-formance in untrained and changing traffic situations, and add features to learnedcontrollers, such as giving priority to emergency vehicles