Pseudouridine synthase 7 impacts Candida albicans rRNA processing and morphological plasticity

RNA can be modified in over 100 distinct ways, and these modifications are critical for function. Pseudouridine synthases catalyse pseudouridylation, one of the most prevalent RNA modifications. Pseudouridine synthase 7 modifies a variety of substrates in Saccharomyces cerevisiae including tRNA, rRNA, snRNA, and mRNA, but the substrates for other budding yeast Pus7 homologues are not known. We used CRISPR-mediated genome editing to disrupt Candida albicans PUS7 and find absence leads to defects in rRNA processing and a decrease in cell surface hydrophobicity. Furthermore, C. albicans Pus7 absence causes temperature sensitivity, defects in filamentation, altered sensitivity to antifungal drugs, and decreased virulence in a wax moth model. In addition, we find C. albicans Pus7 modifies tRNA residues, but does not modify a number of other S. cerevisiae Pus7 substrates. Our data suggests C. albicans Pus7 is important for fungal vigour and may play distinct biological roles than those ascribed to S. cerevisiae Pus7.     

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two-dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid-like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid-like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks.     

Validation of two-layer model for underexpanded hydrogen jets

Previous studies have shown that the two-layer model more accurately predicts hydrogen dispersion than the conventional notional nozzle models without significantly increasing the computational expense. However, the model was only validated for predicting the concentration distribution and has not been adequately validated for predicting the velocity distributions. In the present study, particle imaging velocimetry (PIV) was used to measure the velocity field of an underexpanded hydrogen jet released at 10 bar from a 1.5 mm diameter orifice. The two-layer model was the used to calculate the inlet conditions for a two-dimensional axisymmetric CFD model to simulate the hydrogen jet downstream of the Mach disk. The predicted velocity spreading and centerline decay rates agreed well with the PIV measurements. The predicted concentration distribution was consistent with data from previous planar Rayleigh scattering measurements used to verify the concentration distribution predictions in an earlier study. The jet spreading was also simulated using several widely used notional nozzle models combined with the integral plume model for comparison. These results show that the velocity and concentration distributions are both better predicted by the two-layer model than the notional nozzle models to complement previous studies verifying only the predicted concentration profiles. Thus, this study shows that the two-layer model can accurately predict the jet velocity distributions as well as the concentration distributions as verified earlier. Though more validation studies are needed to improve confidence in the model and increase the range of validity, the present work indicates that the two-layer model is a promising tool for fast, accurate predictions of the flow fields of underexpanded hydrogen jets.     

Superconducting Quantum Metamaterials from High Pressure Melt Infiltration of Metals into Block Copolymer Double Gyroid Derived Ceramic Templates

Mesoscale order can lead to emergent properties including phononic bandgaps or topologically protected states. Block copolymers offer a route to mesoscale periodic architectures, but their use as structure directing agents for metallic materials has not been fully realized. A versatile approach to mesostructured metals via bulk block copolymer self-assembly derived ceramic templates, is demonstrated. Molten indium is infiltrated into mesoporous, double gyroidal silicon nitride templates under high pressure to yield bulk, 3D periodic nanocomposites as free-standing monoliths which exhibit emergent quantum-scale phenomena. Vortices are artificially introduced when double gyroidal indium metal behaves as a type II superconductor, with evidence of strong pinning centers arrayed on the order of the double gyroid lattice size. Sample behavior is reproducible over months, showing high stability. High pressure infiltration of bulk block copolymer self-assembly based ceramic templates is an enabling tool for studying high-quality metals with previously inaccessible architectures, and paves the way for the emerging field of block-copolymer derived quantum metamaterials.     

Converting Alkali Lignin to Biofuels over NiO/HZSM‐5 Catalysts Using a Two‐Stage Reactor

A series of NiO/HZSM‐5 catalysts were used to convert alkali lignin to hydrocarbon biofuels in a two‐stage catalytic pyrolysis system. The results indicated that all NiO/HZSM‐5 catalysts reduced the content of undesirable phenols, furans, and alcohols of the biofuel compared to non‐catalytic treatment. The NiO/HZSM‐5 catalyst with the lowest amount of NiO generated the highest biofuel yield in all catalytic treatments, and it also produced biofuel with the highest content of hydrocarbons. The emission of carbon oxides (CO and CO₂) increased in the treatments with higher‐NiO loading HZSM‐5 due to the redox reaction between NiO and the oxygenated compounds in the bio‐oil. Ni₂SiO₄ was generated in the used NiO/HZSM‐5 catalysts during the high‐temperature pyrolysis process.     

Computational models of germanium point contact detectors

In the crystal bulk of group IV covalent semiconductors such as germanium (Ge), simple analytic models for the valence band structure can provide fast, accurate computations of hole mobility for moderate energy ranges up to a few eV. On the surfaces of these materials, such as on Ge-vacuum or Ge-GeO₂ interfaces, the transport rates differ significantly from the bulk. This can be problematic for both point contact and segmented Ge gamma ray detectors, that require accurate carrier drift rates for computing signal basis sets, which themselves are necessary for the precise determination of gamma-ray induced compton scattering events. While several techniques exist for computing surface hole mobilities, more often than not, these methods are complex to implement, require significant computational resources, and lack the simplicity of bulk models for interpreting results. This paper presents a new technique for computing Ge surface hole mobility that can give a first estimate for the surface transport rates after tuning a physically based computational parameter. This model is used in conjunction with particle-in-cell (PIC) simulations for modeling hole-dynamics inside a Ge p-type point contact detector. The results of our calculations agree with experimental data gathered from Ge p-type point contact detectors at Oak Ridge National Laboratory.     

Logic programming to predict cell fate patterns and retrodict genotypes in organogenesis

Caenorhabditis elegans vulval development is a paradigm system for understanding cell differentiation in the process of organogenesis. Through temporal and spatial controls, the fate pattern of six cells is determined by the competition of the LET-23 and the Notch signalling pathways. Modelling cell fate determination in vulval development using state-based models, coupled with formal analysis techniques, has been established as a powerful approach in predicting the outcome of combinations of mutations. However, computing the outcomes of complex and highly concurrent models can become prohibitive. Here, we show how logic programs derived from state machines describing the differentiation of C. elegans vulval precursor cells can increase the speed of prediction by four orders of magnitude relative to previous approaches. Moreover, this increase in speed allows us to infer, or ‘retrodict’, compatible genomes from cell fate patterns. We exploit this technique to predict highly variable cell fate patterns resulting from dig-1 reduced-function mutations and let-23 mosaics. In addition to the new insights offered, we propose our technique as a platform for aiding the design and analysis of experimental data.     

BeWell: Sensing Sleep,Physical Activities and Social Interactions to Promote Wellbeing

Smartphone sensing and persuasive feedback design is enabling a new generation of wellbeing apps capable of automatically monitoring multiple aspects of physical and mental health. In this article, we present BeWell+ the next generation of the BeWell smartphone wellbeing app, which monitors user behavior along three health dimensions, namely sleep, physical activity, and social interaction. BeWell promotes improved behavioral patterns via feedback rendered as an ambient display on the smartphone’s wallpaper. With BeWell+, we introduce new mechanisms to address key limitations of the original BeWell app; specifically, (1) community adaptive wellbeing feedback, which generalizes to diverse user communities (e.g., elderly, children) by promoting better behavior yet remains realistic to the user’s lifestyle; and, (2) wellbeing adaptive energy allocation, which prioritizes monitoring fidelity and feedback responsiveness on specific health dimensions (e.g., sleep) where the user needs additional help. We evaluate BeWell+ with a 27 person, 19 day field trial. Our findings show that not only can BeWell+ operate successfully on consumer smartphones; but also users understand feedback and respond by taking steps towards leading healthier lifestyles.     

Stochastic optimization of nonlinear energy sinks

Nonlinear energy sinks (NES) are a promising technique to achieve vibration mitigation. Through nonlinear stiffness properties, NES are able to passively and irreversibly absorb energy. Unlike the traditional Tuned Mass Damper (TMD), NES absorb energy from a wide range of frequencies. Many studies have focused on NES behavior and dynamics, but few have addressed the optimal design of NES. Design considerations of NES are of prime importance as it has been shown that NES dynamics exhibit an acute sensitivity to uncertainties. In fact, the sensitivity is so marked that NES efficiency is near-discontinuous and can switch from a high to a low value for a small perturbation in design parameters or loading conditions. This article presents an approach for the probabilistic design of NES which accounts for random design and aleatory variables as well as response discontinuities. In order to maximize the mean efficiency, the algorithm is based on the identification of regions of the design and aleatory space corresponding to markedly different NES efficiencies. This is done through a sequence of approximated sub-problems constructed from clustering, Kriging approximations, a support vector machine, and Monte-Carlo simulations. The refinement of the surrogates is performed locally using a generalized max-min sampling scheme which accounts for the distributions of random variables. The sampling scheme also makes use of the predicted variance of the Kriging surrogates for the selection of aleatory variables values. The proposed algorithm is applied to three example problems of varying dimensionality, all including an aleatory excitation applied to the main system. The stochastic optima are compared to NES optimized deterministically.