Adeno-associated viruses (AAVs) are commonly used for in vivo gene transfer. Nevertheless, AAVs that provide efficient transduction across specific organs or cell populations are needed. Here, we describe AAV-PHP.eB and AAV-PHP.S, capsids that efficiently transduce the central and peripheral nervous systems, respectively. In the adult mouse, intravenous administration of 1×10 11 vector genomes (vg) of AAV-PHP.eB transduced 69% of cortical and 55% of striatal neurons, while 1×10 12 vg AAV-PHP.S transduced 82% of dorsal root ganglion neurons, as well as cardiac and enteric neurons. The efficiency of these vectors facilitates robust co-transduction and stochastic, multicolor labeling for individual cell morphology studies. To support such efforts, we provide methods for labeling a tunable fraction of cells without compromising color diversity. Furthermore, when used with cell type-specific promoters, these AAVs provide targeted gene expression across the nervous system and enable efficient and versatile gene manipulation throughout the nervous system of transgenic and non-transgenic animals.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

In this paper, we propose a system for contextaware

Vehicular ad-hoc networks present great opportunity for information exchange and equal opportunity for abuse. Validating traffic information without imposing significant communication overheads is a hard problem. In this paper, we propose a solution for validating aggregated data. The main idea is to use random checks to probabilistically catch the attacker, and thereby discourage attacks in the network. Our solution relies on PKI based authentication and assumes a tamper-proof service in each car to carry out certain secure operations such as signing and timestamping. We try to keep the set of secure operations as small as possible, in accordance with the principle of economy of mechanism. We show that our solution provides security without significant communication overheads.

Atypical fibroxanthoma (AFX), is a rare type of skin cancer affecting older individuals with sun damaged skin. Since there is limited genomic information about AFX, our study seeks to improve the understanding of AFX through whole-exome and RNA sequencing of 8 matched tumor-normal samples. AFX is a highly mutated malignancy with recurrent mutations in a number of genes, including COL11A1, ERBB4, CSMD3, and FAT1. The majority of mutations identified were UV signature (C>T in dipyrimidines). We observed deletion of chromosomal segments on chr9p and chr13q, including tumor suppressor genes such as KANK1 and CDKN2A, but no gene fusions were found. Gene expression profiling revealed several biological pathways that are upregulated in AFX, including tumor associated macrophage response, GPCR signaling, and epithelial to mesenchymal transition (EMT). To further investigate the presence of EMT in AFX, we conducted a gene expression meta-analysis that incorporated RNA-seq data from dermal fibroblasts and keratinocytes. Ours is the first study to employ high throughput sequencing for molecular profiling of AFX. These data provide valuable insights to inform models of carcinogenesis and additional research towards tumor-directed therapy.

The only way to keep up with the ever-increasing number of cars on roads is through constant change and improvement in the transportation infrastructure. Construction of new roads is constrained by space and financial resources. Therefore, there is a need to devise ways to make optimal use of the existing infrastructure. In this position paper, we describe a lane reservation system for highways. The idea is to allow drivers to reserve a slot on a high-priority lane by paying a premium price. The high-priority lane would provide congestion free travel between any two points on the highway. We describe the design of our system, the challenges that need to be solved and the evaluation methodology we are planning to adopt.

In this paper, we present a system architecture that allows

We report on our efforts to formulate autonomic network repair as a reinforcement-learning problem. Our implemented system is able to learn to efficiently restore network connectivity after a failure.Our research explores a reinforcement-learning (Sutton & Barto 1998) formulation we call cost-sensitive fault remediation (CSFR), which was motivated by problems that arise in sequential decision making for diagnosis and repair. We have considered problems of web-server maintenance and disk-system replacement, and have fully implemented an experimental network-repair application.In cost-sensitive fault remediation, a decision maker is responsible for repairing a system when it breaks down. To narrow down the source of the fault, the decision maker can perform a test action at some cost, and to repair the fault it can carry out a repair action. A repair action incurs a cost and either restores the system to proper functioning or fails. In either case, the system informs the decision maker of the outcome. The decision maker seeks a minimum cost policy for restoring the system to proper functioning.We can find an optimal repair policy via dynamic programming. Let B be the power set of the set of fault states S, which is the set of belief states of the system. For each b ∈ B, define the expected value of action a in belief state s as the expected cost of the action plus the value of the resulting belief state:Here, b i is the belief state resulting from taking action a in belief state b and obtaining outcome i ∈ {0, 1}; it is the subset of b consistent with this outcome. If a is a repair action and i = 1, we define the future value V (b 1 ) = 0, as there is no additional cost incurred once a repair action is successful. In all other cases, the value of a belief state is the minimum action value taken over all available choices:The quantities Pr(b) and c(b, a) are the prior probability of a belief state and expected cost of an action, which can be computed easily from a CSFR specification of the problem. Table 1 illustrates a small CSFR example with two fault states, A and B. The planning process for this example begins with the belief state {A, B}. It considers the test actions DefaultGateway and PingIP and the repair actions FixIP, UseCachedIP, and RenewLease. It does not consider DnsLookup since the action neither provides information (always 0), nor has a non-zero chance of repair. In evaluating the action PingIP, the algorithm finds that outcome 0 has a probability of .25 and outcome 1 has a probability of .75. Its expected cost from belief state {A, B} is then .25(50 + cost({A})) + .75(250 + cost({B})). (1) The expected cost from belief state {A} is computed recursively. Since all test actions have an outcome with an estimated probability of 0, only repair actions are considered. Of these, RenewLease is chosen as the optimal action, with

i Figure 1. Left: User wearing the phone as a pendant, Right: Snapshots of the client running on the phoneThere has been a shift in the focus of indoor localization research from improving accuracy to minimizing infrastructure requirements [4, 6, 1]. The reason is well understood: since location information only serves as a parameter to location-based services, the cost of deploying localization systems should be a minute fraction of the total cost of provisioning location-based services. We demonstrate the possibility of determining user's location indoors based on what the cameraphone "sees". The camera-phone is worn by the user as a pendant( Figure 1) and images are periodically captured and transmitted over GPRS to a web server. The web server has a database of images with their corresponding location. Upon receiving an image, the web server compares it with stored images, and based on the match, estimates user's location. We accomplish this with off-the-shelf image matching algorithms (namely Color Histograms [7], Wavelet Decomposition [2] and Shape Matching [3]) by tailoring them for our purpose. We use three methods for determining location: Naive, Heirarchical and History-based [5]. The advantage of our approach is that neither custom hardware, nor wireless access points are required. Physical objects do not have to be "tagged" and users do not have to carry any device apart from what they already do: a mobile phone. The only cost involved is that of building an image database.We constructed a partial image database for the third floor of the Rutgers Computer Science building with multiple images per "corner" to account for issues such as varying heights of the users, different angles of view, etc. Our experimental results indicate that room-level accuracy can be achieved with more than 90% success probability, and meter-level accuracy can be achieved with more than 80% success probability. The goal of this demo is to illustrate the basic functionality of the system in a limited setting. We will construct an image database for the demo-room on the spot, and demonstrate our system on this database using the Heirarchical approach. We will use a Nokia 6650 phone running a Java client that would capture images and display location, and a server that would host the image database and the location determination algorithms. We will briefly explain the underlying algorithms, the database construction methodology and the key challenges.