FluShuffle and FluResort: new algorithms to identify reassorted strains of the influenza virus by mass spectrometry

Background: Influenza is one of the oldest and deadliest infectious diseases known to man. Reassorted strains of the virus pose the greatest risk to both human and animal health and have been associated with all pandemics of the past century, with the possible exception of the 1918 pandemic, resulting in tens of millions of deaths. We have developed and tested new computer algorithms, FluShuffle and FluResort, which enable reassorted viruses to be identified by the most rapid and direct means possible. These algorithms enable reassorted influenza, and other, viruses to be rapidly identified to allow prevention strategies and treatments to be more efficiently implemented.Results: The FluShuffle and FluResort algorithms were tested with both experimental and simulated mass spectra of whole virus digests. FluShuffle considers different combinations of viral protein identities that match the mass spectral data using a Gibbs sampling algorithm employing a mixed protein Markov chain Monte Carlo (MCMC) method. FluResort utilizes those identities to calculate the weighted distance of each across two or more different phylogenetic trees constructed through viral protein sequence alignments. Each weighted mean distance value is normalized by conversion to a Z-score to establish a reassorted strain.Conclusions: The new FluShuffle and FluResort algorithms can correctly identify the origins of influenza viral proteins and the number of reassortment events required to produce the strains from the high resolution mass spectral data of whole virus proteolytic digestions. This has been demonstrated in the case of constructed vaccine strains as well as common human seasonal strains of the virus. The algorithms significantly improve the capability of the proteotyping approach to identify reassorted viruses that pose the greatest pandemic risk. © 2012 Lun et al.; licensee BioMed Central Ltd.Link_to_subscribed_fulltex

Surgical Approaches to Create Murine Models of Human Wound Healing

Wound repair is a complex biologic process which becomes abnormal in numerous disease states. Although in vitro models have been important in identifying critical repair pathways in specific cell populations, in vivo models are necessary to obtain a more comprehensive and pertinent understanding of human wound healing. The laboratory mouse has long been the most common animal research tool and numerous transgenic strains and models have been developed to help researchers study the molecular pathways involved in wound repair and regeneration. This paper aims to highlight common surgical mouse models of cutaneous disease and to provide investigators with a better understanding of the benefits and limitations of these models for translational applications

Low Temperature Opacities

Previous computations of low temperature Rosseland and Planck mean opacities from Alexander & Ferguson (1994) are updated and expanded. The new computations include a more complete equation of state with more grain species and updated optical constants. Grains are now explicitly included in thermal equilibrium in the equation of state calculation, which allows for a much wider range of grain compositions to be accurately included than was previously the case. The inclusion of high temperature condensates such as Al$_2$O$_3$ and CaTiO$_3$ significantly affects the total opacity over a narrow range of temperatures before the appearance of the first silicate grains. The new opacity tables are tabulated for temperatures ranging from 30000 K to 500 K with gas densities from 10$^{-4}$ g cm$^{-3}$ to 10$^{-19}$ g cm$^{-3}$. Comparisons with previous Rosseland mean opacity calculations are discussed. At high temperatures, the agreement with OPAL and Opacity Project is quite good. Comparisons at lower temperatures are more divergent as a result of differences in molecular and grain physics included in different calculations. The computation of Planck mean opacities performed with the opacity sampling method are shown to require a very large number of opacity sampling wavelength points; previously published results obtained with fewer wavelength points are shown to be significantly in error. Methods for requesting or obtaining the new tables are provided.Comment: 39 pages with 12 figures. To be published in ApJ, April 200

A Longitudinal Study of the Relation between Childhood Activities and Psychosocial Adjustment in Early Adolescence

Background: Although an increasing body of research shows that excessive screen time could impair brain development, whereas non-screen recreational activities can promote the development of adaptive emotion regulation and social skills, there is a lack of comparative research on this topic. Hence, this study examined whether and to what extent the frequency of early-life activities predicted later externalizing and internalizing problems. Methods: In 2012/13, we recruited Kindergarten 3 (K3) students from randomly selected kindergartens in two districts of Hong Kong and collected parent-report data on children’s screen activities and parent–child activities. In 2018/19, we re-surveyed the parents of 323 students (aged 11 to 13 years) with question items regarding their children’s externalizing and internalizing symptoms in early adolescence. Linear regression analyses were conducted to examine the associations between childhood activities and psychosocial problems in early adolescence. Results: Early-life parent–child activities (β = −0.14, p = 0.012) and child-alone screen use duration (β = 0.15, p = 0.007) independently predicted externalizing problems in early adolescence. Their associations with video game exposure (β = 0.19, p = 0.004) and non-screen recreational parent–child activities (β = −0.14, p = 0.004) were particularly strong. Conclusions: Parent–child play time is important for healthy psychosocial development. More efforts should be directed to urge parents and caregivers to replace child-alone screen time with parent–child play time

Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. Methods: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.Comment: 9 figure

Thermochemistry of Alane Complexes for Hydrogen Storage: A Theoretical and Experimental Comparison

Knowledge of the relative stabilities of alane (AlH3) complexes with electron donors is essential for identifying hydrogen storage materials for vehicular applications that can be regenerated by off-board methods; however, almost no thermodynamic data are available to make this assessment. To fill this gap, we employed the G4(MP2) method to determine heats of formation, entropies, and Gibbs free energies of formation for thirty-eight alane complexes with NH3-nRn (R = Me, Et; n = 0-3), pyridine, pyrazine, triethylenediamine (TEDA), quinuclidine, OH2-nRn (R = Me, Et; n = 0-2), dioxane, and tetrahydrofuran (THF). Monomer, bis, and selected dimer complex geometries were considered. Using these data, we computed the thermodynamics of the key formation and dehydrogenation reactions that would occur during hydrogen delivery and alane regeneration, from which trends in complex stability were identified. These predictions were tested by synthesizing six amine-alane complexes involving trimethylamine, triethylamine, dimethylethylamine, TEDA, quinuclidine, and hexamine, and obtaining upper limits of delta G for their formation from metallic aluminum. Combining these computational and experimental results, we establish a criterion for complex stability relevant to hydrogen storage that can be used to assess potential ligands prior to attempting synthesis of the alane complex. Based on this, we conclude that only a subset of the tertiary amine complexes considered and none of the ether complexes can be successfully formed by direct reaction with aluminum and regenerated in an alane-based hydrogen storage system.Comment: Accepted by the Journal of Physical Chemistry

Techstyle Haus

Preliminary design work for the Solar Decathlon 2014 entry Techstyle Haus completed in a wintersession 2013 RISD design studio in Erfurt, Germany taught by Jonathan Knowles. The Solar Decathlon competition challenges twenty collegiate teams to design and build sustainable homes that are powered exclusively by solar energy and incorporate sustainable architecture and design. Techstyle Haus is an international Brown University, RISD and University of Applied Sciences Erfurt,Germany collaboration designing a solar passivehaus out of high performance textiles

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy

Purpose: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. Results: Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s

Haus House

Preliminary design work for the Solar Decathlon 2014 entry Techstyle Haus completed in a wintersession 2013 RISD design studio in Erfurt, Germany taught by Jonathan Knowles. The Solar Decathlon competition challenges twenty collegiate teams to design and build sustainable homes that are powered exclusively by solar energy and incorporate sustainable architecture and design. Techstyle Haus is an international Brown University, RISD and University of Applied Sciences Erfurt,Germany collaboration designing a solar passivehaus out of high performance textiles