Human sperm RNA code senses dietary sugar.

A new study reveals that a high-sugar diet acutely alters human sperm small RNA profiles after 1 week and that these changes are associated with changes in sperm motility. This rapid response by sperm to nutritional fluctuation raises intriguing questions regarding the underlying mechanisms and the potential effects on offspring metabolic health

Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution

In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018, Spotlight

Active Clinical Trials for Personalized Medicine

Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed to efficiently estimate ITRs. In this paper, we propose a cost-effective estimation method from an active learning perspective. In particular, our method recruits only the "most informative" patients (in terms of learning the optimal ITRs) from an ongoing clinical trial. Simulation studies and real-data examples show that our active clinical trial method significantly improves on competing methods. We derive risk bounds and show that they support these observed empirical advantages.Comment: 48 Page, 9 Figures. To Appear in JASA--T&

Attraction of Spiral Waves by Localized Inhomogeneities with Small-World Connections in Excitable Media

Trapping and un-trapping of spiral tips in a two-dimensional homogeneous excitable medium with local small-world connections is studied by numerical simulation. In a homogeneous medium which can be simulated with a lattice of regular neighborhood connections, the spiral wave is in the meandering regime. When changing the topology of a small region from regular connections to small-world connections, the tip of a spiral waves is attracted by the small-world region, where the average path length declines with the introduction of long distant connections. The "trapped" phenomenon also occurs in regular lattices where the diffusion coefficient of the small region is increased. The above results can be explained by the eikonal equation and the relation between core radius and diffusion coefficient.Comment: 5 pages, 4 figure

Synthesis and Biological Applications of Heavy-Metal-Free Semiconductor Nanocrystals

Semiconductor nanocrystals, also called quantum dots (QDs), are an interesting class of materials exhibiting size-tunable optical properties. QDs are attractive for a variety of applications, such as biological sensing and imaging, high color definition display technologies, and photovoltaics. The most widely studied QDs are compound semiconductors of the type CdX and PbX (with X= S, Se, and Te). The absorption and fluorescence emission wavelengths of these QDs span the visible and near infrared (NIR) regions of the electromagnetic spectrum. However, the highly toxic heavy metals Cd and Pb contained in these materials are problematic for their widespread use in commercial applications. Substitution of the heavy metals with Zn and Sn can yield QDs that are less toxic and more environmentally friendly. ZnSe QDs are highly luminescent in the UV-blue region of the spectrum and can be engineered to emit at longer wavelengths by doping them with transition metals, such as Mn or Cu. Encapsulation of ZnSe QDs with an inorganic shell, such as ZnS, has been shown to increase their stability and fluorescence intensity (quantum yield). However, the thermodynamic stability of such core/shell particles must be studied to understand the feasibility and long-term stability of atomically-abrupt interfaces between the core and the shell. The thermodynamic stability of ZnSe/ZnTe and ZnTe/ZnSe core/shell QDs was explored in this study. It was found that ZnSe/ZnTe core/shell QDs are thermodynamically more stable than ZnTe/ZnSe core/shell QDs. Functionalization of the surface of the QDs with biomolecules enables their use in biological sensing and imaging applications. A ZnSe-based QD-DNA biosensor was fabricated and characterized using a novel portable time-domain LED fluorimeter that enables nanosecond fluorescence lifetime measurements. SnSe QDs that absorb near-infrared (NIR) radiation are attractive nano-materials for applications in photovoltaics, photodetectors and photothermal therapy. A new synthesis method for small-size (\u3c 4nm) SnSe QDs was developed that employs an air-stable tin(II) chloride-oleylamine complex and selenium powder dissolved in trioctylphosphine as the precursors. The growth rate and morphology of the nanocrystals were studied as functions of the processing conditions. Optimal synthesis conditions that allow precise control over the final particle size and prevent particle aggregation were identified. The SnSe QDs were coated with a ZnSe shell, capped with 11-mercaptoundecanoic acid and dispersed in aqueous solution to enable bioconjugation with amino-modified biomolecules for biological applications. To meet the increasing demand for QDs, the development of new, highly-efficient processes for their synthesis is required. A continuous flow reactor was developed that enables efficient synthesis of ZnSe QDs using microemulsions as templates for controlling both the size and size distribution of the particles. The operating conditions of the reactor were optimized to maximize particle quality and conversion of precursors

Augmented Weighted Estimators Dealing with Practical Positivity Violation to Causal inferences in a Random Coefficient Model.

The inverse probability of treatment weighted (IPTW) estimator can be used to make causal inferences under two assumptions: (1) no unobserved confounders (ignorability) and (2) positive probability of treatment and of control at every level of the confounders (positivity), but is vulnerable to bias if by chance, the proportion of the sample assigned to treatment, or proportion of control, is zero at certain levels of the confounders. We propose to deal with this sampling zero problem, also known as practical violation of the positivity assumption, in a setting where the observed confounder is cluster identity, i.e., treatment assignment is ignorable within clusters. Specifically, based on a random coefficient model assumed for the potential outcome, we augment the IPTW estimating function with the estimated potential outcomes of treatment (or of control) for clusters that have no observation of treatment (or control). If the cluster-specific potential outcomes are estimated correctly, the augmented estimating function can be shown to converge in expectation to zero and therefore yield consistent causal estimates. The proposed method can be implemented in the existing software, and it performs well in simulated data as well as with real-world data from a teacher preparation evaluation study