Plasma and cavitation dynamics during pulsed laser microsurgery in vivo

We compare the plasma and cavitation dynamics underlying pulsed laser microsurgery in water and in fruit fly embryos (in vivo) - specifically for nanosecond pulses at 355 and 532 nm. We find two key differences. First, the plasma-formation thresholds are lower in vivo - especially at 355 nm - due to the presence of endogenous chromophores that serve as additional sources for plasma seed electrons. Second, the biological matrix constrains the growth of laser-induced cavitation bubbles. Both effects reduce the disrupted region in vivo when compared to extrapolations from measurements in water.Comment: 9 pages, 5 figure

A Super-Fast Distributed Algorithm for Bipartite Metric Facility Location

The \textit{facility location} problem consists of a set of \textit{facilities} $\mathcal{F}$, a set of \textit{clients} $\mathcal{C}$, an \textit{opening cost} $f_i$ associated with each facility $x_i$, and a \textit{connection cost} $D(x_i,y_j)$ between each facility $x_i$ and client $y_j$. The goal is to find a subset of facilities to \textit{open}, and to connect each client to an open facility, so as to minimize the total facility opening costs plus connection costs. This paper presents the first expected-sub-logarithmic-round distributed O(1)-approximation algorithm in the $\mathcal{CONGEST}$ model for the \textit{metric} facility location problem on the complete bipartite network with parts $\mathcal{F}$ and $\mathcal{C}$. Our algorithm has an expected running time of $O((\log \log n)^3)$ rounds, where $n = |\mathcal{F}| + |\mathcal{C}|$. This result can be viewed as a continuation of our recent work (ICALP 2012) in which we presented the first sub-logarithmic-round distributed O(1)-approximation algorithm for metric facility location on a \textit{clique} network. The bipartite setting presents several new challenges not present in the problem on a clique network. We present two new techniques to overcome these challenges. (i) In order to deal with the problem of not being able to choose appropriate probabilities (due to lack of adequate knowledge), we design an algorithm that performs a random walk over a probability space and analyze the progress our algorithm makes as the random walk proceeds. (ii) In order to deal with a problem of quickly disseminating a collection of messages, possibly containing many duplicates, over the bipartite network, we design a probabilistic hashing scheme that delivers all of the messages in expected-$O(\log \log n)$ rounds.Comment: 22 pages. This is the full version of a paper that appeared in DISC 201

Therapeutic potential of interferon-gamma in tuberculosis

Tuberculosis is one of the critical health problems worldwide. The search for ways to improve the results of tuberculosis treatment and overcome drug resistance lies in understanding the pathogenesis of the development of the infectious process. The interferon system, particularly the role of interferon-gamma, has been identified as the main link in the immune response in tuberculosis. The clinical efficacy of interferon-gamma has been studied and evaluated in clinical trials since the end of the last century. There was obtained evidence of the clinical efficacy of interferon-gamma as part of complex therapy. Recent experimental data make it possible to consider interferon-gamma as a promising therapeutic option for the treatment of multidrug-resistant tuberculosis as part of complex therapy worthy of further studies

Lessons from the Congested Clique Applied to MapReduce

The main results of this paper are (I) a simulation algorithm which, under quite general constraints, transforms algorithms running on the Congested Clique into algorithms running in the MapReduce model, and (II) a distributed $O(\Delta)$-coloring algorithm running on the Congested Clique which has an expected running time of (i) $O(1)$ rounds, if $\Delta \geq \Theta(\log^4 n)$; and (ii) $O(\log \log n)$ rounds otherwise. Applying the simulation theorem to the Congested-Clique $O(\Delta)$-coloring algorithm yields an $O(1)$-round $O(\Delta)$-coloring algorithm in the MapReduce model. Our simulation algorithm illustrates a natural correspondence between per-node bandwidth in the Congested Clique model and memory per machine in the MapReduce model. In the Congested Clique (and more generally, any network in the $\mathcal{CONGEST}$ model), the major impediment to constructing fast algorithms is the $O(\log n)$ restriction on message sizes. Similarly, in the MapReduce model, the combined restrictions on memory per machine and total system memory have a dominant effect on algorithm design. In showing a fairly general simulation algorithm, we highlight the similarities and differences between these models.Comment: 15 page

Reactions to uncertainty and the accuracy of diagnostic mammography.

BackgroundReactions to uncertainty in clinical medicine can affect decision making.ObjectiveTo assess the extent to which radiologists' reactions to uncertainty influence diagnostic mammography interpretation.DesignCross-sectional responses to a mailed survey assessed reactions to uncertainty using a well-validated instrument. Responses were linked to radiologists' diagnostic mammography interpretive performance obtained from three regional mammography registries.ParticipantsOne hundred thirty-two radiologists from New Hampshire, Colorado, and Washington.MeasurementMean scores and either standard errors or confidence intervals were used to assess physicians' reactions to uncertainty. Multivariable logistic regression models were fit via generalized estimating equations to assess the impact of uncertainty on diagnostic mammography interpretive performance while adjusting for potential confounders.ResultsWhen examining radiologists' interpretation of additional diagnostic mammograms (those after screening mammograms that detected abnormalities), a 5-point increase in the reactions to uncertainty score was associated with a 17% higher odds of having a positive mammogram given cancer was diagnosed during follow-up (sensitivity), a 6% lower odds of a negative mammogram given no cancer (specificity), a 4% lower odds (not significant) of a cancer diagnosis given a positive mammogram (positive predictive value [PPV]), and a 5% higher odds of having a positive mammogram (abnormal interpretation).ConclusionMammograms interpreted by radiologists who have more discomfort with uncertainty have higher likelihood of being recalled

Quantum Phase Tomography of a Strongly Driven Qubit

The interference between repeated Landau-Zener transitions in a qubit swept through an avoided level crossing results in Stueckelberg oscillations in qubit magnetization. The resulting oscillatory patterns are a hallmark of the coherent strongly-driven regime in qubits, quantum dots and other two-level systems. The two-dimensional Fourier transforms of these patterns are found to exhibit a family of one-dimensional curves in Fourier space, in agreement with recent observations in a superconducting qubit. We interpret these images in terms of time evolution of the quantum phase of qubit state and show that they can be used to probe dephasing mechanisms in the qubit.Comment: 5 pgs, 4 fg