How to build nanoblocks using DNA scaffolds

In recent years there have been a number of proposals to utilize the specificity of DNA based interactions for potential applications in nanoscience. One interesting direction is the self-assembly of micro- and nanoparticle clusters using DNA scaffolds. In this letter we consider a DNA scaffold method to self-assemble clusters of "colored" particles. Stable clusters of microspheres have recently been produced by an entirely different method. Our DNA based approach self-assembles clusters with additional degrees of freedom associated with particle permutation. We demonstrate that in the non-equilibrium regime of irreversible binding the self-assembly process is experimentally feasible. These color degrees of freedom may allow for more diverse intercluster interactions essential for hierarchical self-assembly of larger structures.Comment: 4 pages, 2 figures ; epl forma

Response of finite-time particle detectors in non-inertial frames and curved spacetime

The response of the Unruh-DeWitt type monopole detectors which were coupled to the quantum field only for a finite proper time interval is studied for inertial and accelerated trajectories, in the Minkowski vacuum in (3+1) dimensions. Such a detector will respond even while on an inertial trajctory due to the transient effects. Further the response will also depend on the manner in which the detector is switched on and off. We consider the response in the case of smooth as well as abrupt switching of the detector. The former case is achieved with the aid of smooth window functions whose width, $T$, determines the effective time scale for which the detector is coupled to the field. We obtain a general formula for the response of the detector when a window function is specified, and work out the response in detail for the case of gaussian and exponential window functions. A detailed discussion of both $T \rightarrow 0$ and $T \rightarrow \infty$ limits are given and several subtlities in the limiting procedure are clarified. The analysis is extended for detector responses in Schwarzschild and de-Sitter spacetimes in (1+1) dimensions.Comment: 29 pages, normal TeX, figures appended as postscript file, IUCAA Preprint # 23/9

The rate of quasiparticle recombination probes the onset of coherence in cuprate superconductors

The condensation of an electron superfluid from a conventional metallic state at a critical temperature $T_c$ is described well by the BCS theory. In the underdoped copper-oxides, high-temperature superconductivity condenses instead from a nonconventional metallic "pseudogap" phase that exhibits a variety of non-Fermi liquid properties. Recently, it has become clear that a charge density wave (CDW) phase exists within the pseudogap regime, appearing at a temperature $T_{CDW}$ just above $T_c$. The near coincidence of $T_c$ and $T_{CDW}$, as well the coexistence and competition of CDW and superconducting order below $T_c$, suggests that they are intimately related. Here we show that the condensation of the superfluid from this unconventional precursor is reflected in deviations from the predictions of BSC theory regarding the recombination rate of quasiparticles. We report a detailed investigation of the quasiparticle (QP) recombination lifetime, $\tau_{qp}$, as a function of temperature and magnetic field in underdoped HgBa$_{2}$CuO$_{4+\delta}$ (Hg-1201) and YBa$_{2}$Cu$_{3}$O$_{6+x}$ (YBCO) single crystals by ultrafast time-resolved reflectivity. We find that $\tau_{qp}(T)$ exhibits a local maximum in a small temperature window near $T_c$ that is prominent in underdoped samples with coexisting charge order and vanishes with application of a small magnetic field. We explain this unusual, non-BCS behavior by positing that $T_c$ marks a transition from phase-fluctuating SC/CDW composite order above to a SC/CDW condensate below. Our results suggest that the superfluid in underdoped cuprates is a condensate of coherently-mixed particle-particle and particle-hole pairs

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

Aqueous Angiography with Fluorescein and Indocyanine Green in Bovine Eyes.

PurposeWe characterize aqueous angiography as a real-time aqueous humor outflow imaging (AHO) modality in cow eyes with two tracers of different molecular characteristics.MethodsCow enucleated eyes (n = 31) were obtained and perfused with balanced salt solution via a Lewicky AC maintainer through a 1-mm side-port. Fluorescein (2.5%) or indocyanine green (ICG; 0.4%) were introduced intracamerally at 10 mm Hg individually or sequentially. With an angiographer, infrared and fluorescent images were acquired. Concurrent anterior segment optical coherence tomography (OCT) was performed, and fixable fluorescent dextrans were introduced into the eye for histologic analysis of angiographically positive and negative areas.ResultsAqueous angiography in cow eyes with fluorescein and ICG yielded high-quality images with segmental patterns. Over time, ICG maintained a better intraluminal presence. Angiographically positive, but not negative, areas demonstrated intrascleral lumens with anterior segment OCT. Aqueous angiography with fluorescent dextrans led to their trapping in AHO pathways. Sequential aqueous angiography with ICG followed by fluorescein in cow eyes demonstrated similar patterns.ConclusionsAqueous angiography in model cow eyes demonstrated segmental angiographic outflow patterns with either fluorescein or ICG as a tracer.Translational relevanceFurther characterization of segmental AHO with aqueous angiography may allow for intelligent placement of trabecular bypass minimally invasive glaucoma surgeries for improved surgical results

Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201

A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders

Publicado en Lecture Notes in Computer Science.The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using data from heterogeneous sources to estimate the evolu- tion of a given disease for the particular case of a certain patient. In recent years, next-generation sequencing data have boosted cancer prediction by supplying gene-expression information that has allowed diverse machine learning algorithms to supply valuable solutions to the problem of cancer subtype classification, which has surely contributed to better estimation of patient’s response to diverse treatments. However, the efficacy of these models is seriously affected by the existing imbalance between the high dimensionality of the gene expression feature sets and the number of sam- ples available for a particular cancer type. To counteract what is known as the curse of dimensionality, feature selection and extraction methods have been traditionally applied to reduce the number of input variables present in gene expression datasets. Although these techniques work by scaling down the input feature space, the prediction performance of tradi- tional machine learning pipelines using these feature reduction strategies remains moderate. In this work, we propose the use of the Pan-Cancer dataset to pre-train deep autoencoder architectures on a subset com- posed of thousands of gene expression samples of very diverse tumor types. The resulting architectures are subsequently fine-tuned on a col- lection of specific breast cancer samples. This transfer-learning approach aims at combining supervised and unsupervised deep learning models with traditional machine learning classification algorithms to tackle the problem of breast tumor intrinsic-subtype classification.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Characterization of the Prophage Repertoire of African Salmonella Typhimurium ST313 Reveals High Levels of Spontaneous Induction of Novel Phage BTP1

In the past 30 years,Salmonella bloodstream infections have become a significant health problem in sub-Saharan Africa and are responsible for the deaths of anestimated 390,000 people each year. The disease is predominantly caused by a recently described sequence type of SalmonellaTyphimurium: ST313, which has a distinctive set of prophage sequences. We have thoroughly characterized the ST313-associated prophages both genetically and experimentally. ST313 representative strain D23580 contains five full-length prophages: BTP1, Gifsy-2D23580, ST64BD23580, Gifsy-1D23580,and BTP5. We show that commonS.Typhimurium prophages Gifsy-2, Gifsy-1, andST64B are inactivated in ST313 by mutations. Prophage BTP1 was found to be a functional novel phage, and the first isolate of the proposed new species “Salmonellavirus BTP1”, belonging to the P22virusgenus. Surprisingly,∼109BTP1 virus particlesperml were detected in the supernatant of non-induced, stationary-phase culturesof strain D23580, representing the highest spontaneously induced phage titer so farreported for a bacterial prophage. High spontaneous induction is shown to be anintrinsic property of prophage BTP1, and indicates the phage-mediated lysis of around0.2% of the lysogenic population. The fact that BTP1 is highly conserved in ST313 poses interesting questions about the potential fitness costs and benefits of novel prophagesin epidemicS.Typhimurium ST313