Breaking survival barriers in breast cancer- A case of CNS metastasis

One in every eight women worldwide will be diagnosed to be suffering with breast cancer in their lifetime. It is vital not only to diagnose the disease early but also to refer it to a tertiary care center for the most appropriate or standard anti-cancer treatment without delay. The outcomes of such therapy can be a game changer for the patient. Screening mammography is only having a small reduction in mortality of breast cancer. Here, we present a clinical report of one patient who had Her2 Neu positive disease with early central nervous system (CNS) metastasis and responded extremely well to anti her2 Neu therapy with chemotherapy and has survived over six years now on treatment

Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation

Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot

Distribution of Contact Pressure and Stresses under Skirted Footings

Skirted footings posses many novel characteristics which render them eminently suitable for construction of structures in situations involving heavy loads and poor soil conditions with promise of economy. The results of the present investigations will help considerably to understand a detailed picture of the complex phenomenon of contact pressure distribution and vertical stress distribution in soil under skirted footings

Experimental Results of Concurrent Learning Adaptive Controllers

Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very di fferent. Concurrent learning adaptive control uses specifi cally selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT's RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110688)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 0645960)Boeing Scientific Research Laboratorie

Experimental Validation of Plant Peroxisomal Targeting Prediction Algorithms by Systematic Comparison of In Vivo Import Efficiency and In Vitro PTS1 Binding Affinity

Most peroxisomal matrix proteins possess a C-terminal targeting signal type 1 (PTS1). Accurate prediction of functional PTS1 sequences and their relative strength by computational methods is essential for determination of peroxisomal proteomes in silico but has proved challenging due to high levels of sequence variability of non-canonical targeting signals, particularly in higher plants, and low levels of availability of experimentally validated non-canonical examples. In this study, in silico predictions were compared with in vivo targeting analyses and in vitro thermodynamic binding of mutated variants within the context of one model targeting sequence. There was broad agreement between the methods for entire PTS1 domains and position-specific single amino acid residues, including residues upstream of the PTS1 tripeptide. The hierarchy Leu>Met>Ile>Val at the C-terminal position was determined for all methods but both experimental approaches suggest that Tyr is underweighted in the prediction algorithm due to the absence of this residue in the positive training dataset. A combination of methods better defines the score range that discriminates a functional PTS1. In vitro binding to the PEX5 receptor could discriminate among strong targeting signals while in vivo targeting assays were more sensitive, allowing detection of weak functional import signals that were below the limit of detection in the binding assay. Together, the data provide a comprehensive assessment of the factors driving PTS1 efficacy and provide a framework for the more quantitative assessment of the protein import pathway in higher plants

Biosand Filter for Removal of Chemical Contaminants From Water

Numerous reports by the United Nations and the World Health Organization have indicated a significant worldwide problem with water pollution and inaccessibility to potable drinking water. Due to technological and economical barriers, the problem with water pollution is particularly more serious for under-developed and developing countries. The present study is aimed at designing, constructing and evaluating a cost-effective biosand filter was undertaken. Results indicated the removal of up to 80% total hardness, 86% chlorides, 96% turbidity and 90% colour. Moreover, the filter's performance was appraised by the absence of E. coli in the filtered sample. The filter describes the proven bioremediation technology and its ability to empower at-risk populations to use naturally occurring biology and readily available materials as a sustainable way to achieve the health benefits of safe drinking water

In-network processing of nearest neigbor queries for wireless sensor networks

Abstract. Wireless sensor networks have been widely used for civilian and military applications, such as environmental monitoring and vehicle tracking. The sensor nodes in the network have the abilities to sense, store, compute and communicate. To enable object tracking applications, spatial queries such as nearest neighbor queries are to be supported in these networks. The queries can be injected by the user at any sensor node. Due to the limited power supply for sensor nodes, energy efficiency is the major concern in query processing. Centralized data storage and query processing schemes do not favor energy efficiency. In this paper, we propose a distributed scheme called DNN for in-network processing of nearest neighbor queries. A cost model is built to analyze the performance of DNN. Experimental results show that DNN outperforms the centralized scheme significantly in terms of energy consumption and network lifetime.

Inherent-Structure Dynamics and Diffusion in Liquids

The self-diffusion constant D is expressed in terms of transitions among the local minima of the potential (inherent structure, IS) and their correlations. The formulae are evaluated and tested against simulation in the supercooled, unit-density Lennard-Jones liquid. The approximation of uncorrelated IS-transition (IST) vectors, D_{0}, greatly exceeds D in the upper temperature range, but merges with simulation at reduced T ~ 0.50. Since uncorrelated IST are associated with a hopping mechanism, the condition D ~ D_{0} provides a new way to identify the crossover to hopping. The results suggest that theories of diffusion in deeply supercooled liquids may be based on weakly correlated IST.Comment: submitted to PR

Actuator Constrained Trajectory Generation and Control for Variable-Pitch Quadrotors

Control and trajectory generation algorithms for a quadrotor helicopter with variable-pitch propellers are presented. The control law is not based on near-hover assumptions, allowing for large attitude deviations from hover. The trajectory generation algorithm ts a time-parametrized polynomial through any number of way points in R3, with a closed-form solution if the corresponding way point arrival times are known a priori. When time is not specifi ed, an algorithm for fi nding minimum-time paths subject to hardware actuator saturation limitations is presented. Attitude-specifi c constraints are easily embedded in the polynomial path formulation, allowing for aerobatic maneuvers to be performed using a single controller and trajectory generation algorithm. Experimental results on a variable pitch quadrotor demonstrate the control design and example trajectories.National Science Foundation (U.S.) (Graduate Research Fellowship under Grant No. 0645960