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Mouse models of neuropsychiatric diseases provide a platform for mechanistic understanding and development of new therapies. We previously demonstrated that knockout of the mouse homologue of CNTNAP2, in which mutant forms cause Cortical Dysplasia and Focal Epilepsy syndrome (CDFE), displays many features parallel to the human disorder. Since CDFE has high penetrance for autism spectrum disorder (ASD) we performed an in vivo screen for drugs that treat abnormal social behavior in Cntnap2 mutant mice and found that acute administration of the neuropeptide oxytocin improved social deficits. We found a decrease in the number of oxytocin immunoreactive neurons in the paraventricular nucleus (PVN) of the hypothalamus in mutant mice and an overall decrease in brain oxytocin levels. Administration of a selective melanocortin receptor 4 agonist, which causes endogenous oxytocin release, also acutely rescued the social deficits, an effect blocked by an oxytocin antagonist. We confirmed that oxytocin neurons mediated the behavioral improvement by activating endogenous oxytocin neurons in the paraventricular hypothalamus with Designer Receptors Exclusively Activated by Designer Drugs (DREADD). Last, we showed that chronic early postnatal treatment with oxytocin led to more lasting behavioral recovery and restored oxytocin immunoreactivity in the PVN. These data demonstrate dysregulation of the oxytocin system in Cntnap2 knockout mice and suggest that there may be critical developmental windows for optimal treatment.

Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation. There are many challenges in implementation of such systems: energy dissipation and clustering being one of them. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Sensor node clustering is another very important optimization problem. Nodes that are clustered together will easily be able to communicate with each other. Considering energy as an optimization parameter while clustering is imperative. In this paper we study the theoretical aspects of the clustering problem in sensor networks with application to energy optimization. We illustrate an optimal algorithm for clustering the sensor nodes such that each cluster (which has a master) is balanced and the total distance between sensor nodes and master nodes is minimized. Balancing the clusters is needed for evenly distributing the load on all master nodes. Minimizing the total distance helps in reducing the communication overhead and hence the energy dissipation. This problem (which we call balanced k-clustering) is modeled as a mincost flow problem which can be solved optimally using existing techniques.

The use of white space in fixed-die standard-cell placement is an effective way to improve routability. In this paper, we present a white space allocation approach that dynamically assigns white space according to the congestion distribution of the placement. In the topdown placement flow, white space is assigned to congested regions using a smooth allocating function. A post allocation optimization step is taken to further improve placement quality. Experimental results show that the proposed allocation approach, combined with a multilevel placement flow, significantly improves placement routability and layout quality.In our experiments, we compared our placement tool with two other fixed-die placers using an industrial place and route flow. Placements created by all three tools have been routed with an industrial router (Warp Route of Cadence). Compared with a leadingedge industrial tool, our placer produces placements with similar or better routability and on average 8.8% shorter routed wirelength. Furthermore, our tool produces placement that runs faster through the Warp Route compared with the industrial tool. Compared with a state-of-the-art academic placement tool (Capo/MetaPlacer), our placer shows ability to produce more routable placements: for 15 out of all 16 benchmarks our placer's outputs are routable while Capo/MetaPlacer only creates 4 routable placements.

Congestion is one of the fundamental issues in VLSI physical design. In this paper, we propose two congestion estimation approaches for early placement stages. First, we theoretically analyze the peak congestion value of the design and experimentally validate the estimation approach. Second, we estimate regional congestion in the early topdown placement. This is done by c o m bining the wirelength distribution model and inter-region wire estimation. Both approaches are based on the well known Rent's rule, which is previously used for wirelength estimation. This is the rst attempt to predict congestion using Rent's rule. The estimation results are compared with the layout after placement and global routing. Experiments on large industry circuits show that the early congestion estimation based on Rent's rule is a promising approach.

Design hierarchy plays an important role in timing-driven placement for large circuits. In this paper, we present a new methodology for delay budgeting based timing-driven placement. A novel slack assignment approach is described as well as its application on delay budgeting with design hierarchy information. The proposed timing-driven placement flow is implemented into a placement tool named Dragon (timing-driven mode), and evaluated using an industrial place and route flow. Compared to Cadence QPlace, timing-driven Dragon generates placement results with shorter clock cycle and better routability.

As technology advances, more and more issues need to be considered in the placement stage, e.g., wirelength, congestion, timing, coupling. It is very hard to consider all of them together at the same time. Thus it is good if we can optimize one cost function without affecting others. In this paper, we will study methods to optimize congestion in placement without inflicting degradations/violations in other objectives or constraint. We give a mathematical equation to predict the overflow within a region using a normal distribution approximation. According to experiments, this equation does give a good estimation of overflow. We used this equation to find the smallest regions which have enough routing resource to alleviate the congestion and propose the flexible expansion scheme in our multi-center congestion reduction (MC'R) algorithm. Experimental results show that generally there is a correlation between the amount of reduction in congestion and the amount of change made to the placement: the more we change the placement, the more reduction in congestion we will get. However, the flexible expansion scheme is very effective in helping us reduce congestion while make only little change to the placement. Comparing to the full expansion scheme (49% congestion reduction and 6.5% change in placement), the flexible expansion scheme together with MC'R algorithm can reduce congestion by almost the same amount (42%) with much less change made to the placement (1.8%).

Three novel alkaline earth metal salts (Mg2+(1), Ca2+(2), Sr2+(3)) of 5,5′-dinitramino-3,3′-methylene-1H-1,2,4-bistriazolate (DNAMT) have been synthesized in a simple and straightforward manner. The crystal structures of 1-3 were confirmed by single-crystal...