Mapping QTL for sex and growth traits in Salt-Tolerant Tilapia (Oreochromis spp. X O. mossambicus)

In aquaculture, growth and sex are economically important traits. To accelerate genetic improvement in increasing growth in salt-tolerant tilapia, we conducted QTL mapping for growth traits and sex with an F2 family, including 522 offspring and two parents. We used 144 polymorphic microsatellites evenly covering the genome of tilapia to genotype the family. QTL analyses were carried out using interval mapping for all individuals, males and females in the family, respectively. Using all individuals, three suggestive QTL for body weight, body length and body thickness respectively were detected in LG20, LG22 and LG12 and explained 2.4% to 3.1% of phenotypic variance (PV). When considering only males, five QTL for body weight were detected on five LGs, and explained 4.1 to 6.3% of PV. Using only females from the F2 family, three QTL for body weight were detected on LG1, LG6 and LG8, and explained 7.9–14.3% of PV. The QTL for body weight in males and females were located in different LGs, suggesting that in salt-tolerant tilapia, different set of genes ‘switches’ control the growth in males and females. QTL for sex were mapped on LG1 and LG22, indicating multigene sex determination in the salt-tolerant tilapia. This study provides new insights on the locations and effects of QTL for growth traits and sex, and sets the foundation for fine mapping for future marker-assisted selection for growth and sex in salt-tolerant tilapia aquaculture

The Research of Sequential Images: Rebuilding of Gray (Position) ~ Time Function on Direction Lines and Their Applications

Contrasted with other information carriers, such as speech and text, images contains larger amount of information, especially in sequential images, that is waiting to be exploited, in particular the dynamic information of correlation, difference, and temporal relationship between different frames. This dynamic information contributes a great deal in analysis of 4D images. This paper proposes a method for detecting dynamic information from sequential images, based on the rebuilding of their gray (position)~time function on direction lines, an approach that has been analyzed and studied extensively on the setting of various direction lines. This method is based on motion that is presented on sequential images. In particular, the method, Omni directional M-mode Echocardiography system, which we have studied extensively, will be described leading to a robust way of diagnosing heart diseases

Inert Higgs Dark Matter for New CDF W-boson Mass and Detection Prospects

The $W$-boson mass, which was recently measured at FermiLab, suggests the presence of new multiplets beyond the Standard Model (SM). One of the minimal extensions of the SM is to introduce an additional scalar doublet, in which the non-SM scalars can enhance $W$-boson mass via the loop corrections. On the other hand, with a proper discrete symmetry, the lightest new scalar in the doublet can be stable and play the role of dark matter particle. We show that the inert two Higgs doublet model can naturally handle the new $W$-boson mass without violating other constraints, and the preferred dark matter mass is between $54$ and $74$ GeV. We identify three feasible parameter regions for the thermal relic density: the $SA$ co-annihilation, the Higgs resonance, and the $SS \to WW^*$ annihilation. We find that the first region can be fully tested by the HL-LHC, the second region will be tightly constrained by direct detection experiments, and the third region could yield detectable GeV gamma-ray and antiproton signals in the Galaxy that may have been observed by Fermi-LAT and AMS-02.Comment: 8 pages, 5 figure

Broadband NIR-emitting Te cluster-doped glass for smart light source towards night-vision and NIR spectroscopy applications

Broadband near-infrared (NIR)-emitting materials are crucial components of the next generation of smart NIR light sources based on blue light-emitting diodes (LEDs). Here, we report a Te cluster-doped borate glass, which exhibits ultra-broadband emission around 980 nm with a full-width at half-maximum (FWHM) of 306 nm under blue light excitation. We propose adjustments of glass chemistry and processing condition as a means for topo-chemical tailoring of the NIR photoemission characteristics in such materials. Through implementing strongly reducing conditions during glass melting, Te clusters with broad NIR photoluminescence can be generated and stabilized once the melt is vitrified to the glassy state. Tunability of the NIR emission peak over the wavelength range of 904 to 1026 nm is possible in this way, allowing for fine adjustments of spectral properties relative to the stretching vibrations of common chemical bonds, for example, in water, proteins, and fats. This potentially enables high sensitivity in NIR spectroscopy. We further demonstrate potential application of glass-converted LEDs in night vision.</p

Application of the Gaussian mixture model in pulsar astronomy -- pulsar classification and candidates ranking for {\it Fermi} 2FGL catalog

Machine learning, algorithms to extract empirical knowledge from data, can be used to classify data, which is one of the most common tasks in observational astronomy. In this paper, we focus on Bayesian data classification algorithms using the Gaussian mixture model and show two applications in pulsar astronomy. After reviewing the Gaussian mixture model and the related Expectation-Maximization algorithm, we present a data classification method using the Neyman-Pearson test. To demonstrate the method, we apply the algorithm to two classification problems. Firstly, it is applied to the well known period-period derivative diagram, where we find that the pulsar distribution can be modeled with six Gaussian clusters, with two clusters for millisecond pulsars (recycled pulsars) and the rest for normal pulsars. From this distribution, we derive an empirical definition for millisecond pulsars as $\frac{\dot{P}}{10^{-17}} \leq3.23(\frac{P}{100 \textrm{ms}})^{-2.34}$. The two millisecond pulsar clusters may have different evolutionary origins, since the companion stars to these pulsars in the two clusters show different chemical composition. Four clusters are found for normal pulsars. Possible implications for these clusters are also discussed. Our second example is to calculate the likelihood of unidentified \textit{Fermi} point sources being pulsars and rank them accordingly. In the ranked point source list, the top 5% sources contain 50% known pulsars, the top 50% contain 99% known pulsars, and no known active galaxy (the other major population) appears in the top 6%. Such a ranked list can be used to help the future follow-up observations for finding pulsars in unidentified \textit{Fermi} point sources.Comment: 9 pages, 4 figures, accepted by MNRA

Cortical Neural Stem Cell Lineage Progression Is Regulated by Extrinsic Signaling Molecule Sonic Hedgehog.

Neural stem cells (NSCs) in the prenatal neocortex progressively generate different subtypes of glutamatergic projection neurons. Following that, NSCs have a major switch in their progenitor properties and produce γ-aminobutyric acid (GABAergic) interneurons for the olfactory bulb (OB), cortical oligodendrocytes, and astrocytes. Herein, we provide evidence for the molecular mechanism that underlies this switch in the state of neocortical NSCs. We show that, at around E16.5, mouse neocortical NSCs start to generate GSX2-expressing (GSX2+) intermediate progenitor cells (IPCs). In&nbsp;vivo lineage-tracing study revealed that GSX2+ IPC population gives rise not only to OB interneurons but also to cortical oligodendrocytes and astrocytes, suggesting that they are a tri-potential population. We demonstrated that Sonic hedgehog signaling is both necessary and sufficient for the generation of GSX2+ IPCs by reducing GLI3R protein levels. Using single-cell RNA sequencing, we identify the transcriptional profile of GSX2+ IPCs and the process of the lineage switch of cortical NSCs

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users