339 research outputs found
Prediction of Glycerol-Effect on Antigen-Antibody Binding Affinity from Molecular Dynamics Simulations
Many biological and biotechnological processes are controlled by protein-protein interactions in solution. In order to understand, predict and optimize such processes, it is valuable to understand how additives such as salts, sugars, polyols and denaturants affect protein-protein interactions. Currently, no methodology to foretell the effect of additives on protein-protein interactions has been established and frequently and extensive empirical screening to identify additives beneficial to the protein process is resorted to. In this work, we developed a methodology enabling the prediction of the additive-effect on the protein reaction equilibrium. The only prerequisite is that the atomic structure of the protein reactants and products are known. The methodology is based on the thermodynamic model for preferential interactions and makes use of molecular dynamics simulations to gauge additive-protein interactions. In order to validate our methodology, the change in binding affinity of the antibody fragment Y32S Fv D1.3 for lysozyme in the presence of varying glycerol concentrations is being calculated and the results will be compared with experimental data from literature. Finally, our methodology will be used to predict the glycerol effect on the binding affinity of wild type Fv D1.3 and various mutants.Singapore-MIT Alliance (SMA
Pulse-lavage brushing followed by hydrogen peroxide-gauze packing for bone-bed preparation in cemented total hip arthroplasty : a bovine model
To compare the effectiveness of pulse-lavage brushing followed by hydrogen peroxide-gauze packing with either technique alone or normal-saline irrigation in bone-bed preparation for cemented total hip arthroplasty. 44 fresh-frozen ox femoral canals were prepared for cemented total hip arthroplasty using 4 techniques: normal-saline irrigation, pulse-lavage brushing, hydrogen peroxide-soaked gauze packing, and a combination of the latter 2 techniques. The maximum tensile pull-out force required to separate the prosthesis from the femoral canal was measured as an indicator of the strength of the cement-bone interface. The mean pull-out force to separate the prosthesis from the femoral canal was significantly higher in specimens prepared with pulse-lavage brushing followed by hydrogen peroxide-soaked gauze packing or pulse-lavage brushing alone than those prepared with normal-saline irrigation or hydrogen peroxide-soaked gauze packing alone 300(p<0.001). Pulse-lavage brushing is more effective at cleansing the femoral canal and increasing mechanical strength at the cement-bone interface than preparation with normal-saline irrigation or hydrogen peroxide-soaked gauze packing.<br /
R-MNet: A Perceptual Adversarial Network for Image Inpainting
Facial image inpainting is a problem that is widely studied, and in recent
years the introduction of Generative Adversarial Networks, has led to
improvements in the field. Unfortunately some issues persists, in particular
when blending the missing pixels with the visible ones. We address the problem
by proposing a Wasserstein GAN combined with a new reverse mask operator,
namely Reverse Masking Network (R-MNet), a perceptual adversarial network for
image inpainting. The reverse mask operator transfers the reverse masked image
to the end of the encoder-decoder network leaving only valid pixels to be
inpainted. Additionally, we propose a new loss function computed in feature
space to target only valid pixels combined with adversarial training. These
then capture data distributions and generate images similar to those in the
training data with achieved realism (realistic and coherent) on the output
images. We evaluate our method on publicly available dataset, and compare with
state-of-the-art methods. We show that our method is able to generalize to
high-resolution inpainting task, and further show more realistic outputs that
are plausible to the human visual system when compared with the
state-of-the-art methods.Comment: 10 pages, 7 figures, 3 table
Microplastics in agroecosystems: A review of effects on soil biota and key soil functions
Contamination of soils in agroecosystems with microplastics (MPs) is of increasing concern. The contamination of the environment/farmland soils with MPs (1 ”m to 5 mm sized particles) and nanoplastics (NPs; <1 ”m sized particles) is causing numerous effects on ecological soil functions and human health. MPs enter the soil via several sources, either from intentional plastic use (e.g., plastic mulch, plastic greenhouses, plastic-coated products) or indirectly from the input of sewage sludge, compost, or irrigation water that is contaminated with plastic. Once in the soil, plastic debris can have various impacts such as changes in soil functions and physicochemical properties and it affects soil organisms due to its toxic behavior. This review paper describes the different effects of plastic waste to understand the consequences for agricultural productivity. Furthermore, we identify knowledge gaps and highlight the required approaches, indicating future research directions on sources, transport, and fate of MPs in soils to improve our understanding of various unspecified abiotic and biotic impacts of MP pollution in agroecosystems
Transdisciplinary learning: Transformative collaborations between students, industry, academia and communities.
Background and objectives of the case An analogy: Imagine you are invited to a dinner party, but instead of a stuffy sit-down affair, your host asks you to bring your favourite ingredient, and together you prepare a delicious feast of unique and distinct flavours. UTSâs transdisciplinary initiatives are changing the shape of higher education and forging innovative partnerships by bringing together diverse professional fields. With a focus on practice-based and problem-focused learning, UTS educational programs combine the strengths of multiple disciplines, industries, public sector organisations, and the community to turn real-world problems into rewarding opportunities for education and also âlearning for a lifetimeâ. In place of the limitations of artificial disciplinary boundaries, transdisciplinary learning practices create synergistic and innovative approaches to grappling with complex applied challenges. Students, researchers, practitioners, community members and other stakeholders combine their knowledge, tools, techniques, methods, theories, concepts, as well as cultural and personal perspectives. By understanding problems holistically, the solutions that emerge are bold, innovative, and creative, as well as mutually beneficial. We view this as the future of education: good to work with, and good to think with â problem solving for (and with) industry and society. The Faculty of Transdisciplinary Innovation is re-imagining how education, research, and professional practice can work together to navigate todayâs complex problems, and create commercially attractive and socially responsible futures. We also practice what we preach: for example, staff professional development to enact these models in our own teaching; educational programs to provide experiential learning around problem solving within a rapidly-changing environment involving students from across different disciplines and cultural backgrounds; as well as policy development and research on todayâs pressing âwicked problemsâ with industry and government. Primary objectives of this next practice concept of transdisciplinary learning, include: - To promote a shift in industry-university engagement from producing âknowledge for societyâ to co-generating âknowledge with societyâ; - To build a resilient ecosystem for co-learning; - To create and sustain future-oriented degree programs with collaboration between industry, government, and community at the centre, geared to prepare our graduates for the complex challenges of a networked world; - To create an agile and responsive industry-university lab environment for generating and testing new experimental models; - To enable industry â by collaborating with our students and academics â to see their problems from a fresh perspective, often through different and revealing lenses, and to notice opportunities and spot challenges that may have otherwise been overlooked; - To prepare students to lead innovation in a rapidly-changing and challenging world; and - To graduate students who are âcomplexity-fluentâ, systems thinkers, creative problem-posers and -solvers, and imaginative, ethical citizens
Forced Stratified Turbulence: Successive Transitions with Reynolds Number
Numerical simulations are made for forced turbulence at a sequence of
increasing values of Reynolds number, R, keeping fixed a strongly stable,
volume-mean density stratification. At smaller values of R, the turbulent
velocity is mainly horizontal, and the momentum balance is approximately
cyclostrophic and hydrostatic. This is a regime dominated by so-called pancake
vortices, with only a weak excitation of internal gravity waves and large
values of the local Richardson number, Ri, everywhere. At higher values of R
there are successive transitions to (a) overturning motions with local
reversals in the density stratification and small or negative values of Ri; (b)
growth of a horizontally uniform vertical shear flow component; and (c) growth
of a large-scale vertical flow component. Throughout these transitions, pancake
vortices continue to dominate the large-scale part of the turbulence, and the
gravity wave component remains weak except at small scales.Comment: 8 pages, 5 figures (submitted to Phys. Rev. E
R-MNet: a perceptual adversarial network for image inpainting
Facial image inpainting is a problem that is widely studied, and in recent years the introduction of Generative Adversarial Networks, has led to improvements in the field. Unfortunately some issues persists, in particular when blending the missing pixels with the visible ones. We address the problem by proposing a Wasserstein GAN combined with a new reverse mask operator, namely Reverse Masking Network (R-MNet), a perceptual adversarial network for image inpainting. The reverse mask operator transfers the reverse masked image to the end of the encoder-decoder network leaving only valid pixels to be inpainted. Additionally, we propose a new loss function computed in feature space to target only valid pixels combined with adversarial training. These then capture data distributions and generate images similar to those in the training data with achieved realism (realistic and coherent) on the output images. We evaluate our method on publicly available dataset, and compare with state-of-the-art methods. We show that our method is able to generalize to high-resolution inpainting task, and further show more realistic outputs that are plausible to the human visual system when compared with the state-of-the-art methods. https://github.com/Jireh-Jam/R-MNet-Inpainting-kera
The Ks-band Tully-Fisher Relation - A Determination of the Hubble Parameter from 218 ScI Galaxies and 16 Galaxy Clusters
The value of the Hubble Parameter (H0) is determined using the
morphologically type dependent Ks-band Tully-Fisher Relation (K-TFR). The slope
and zero point are determined using 36 calibrator galaxies with ScI morphology.
Calibration distances are adopted from direct Cepheid distances, and group or
companion distances derived with the Surface Brightness Fluctuation Method or
Type Ia Supernova. Distances are determined to 16 galaxy clusters and 218 ScI
galaxies with minimum distances of 40.0 Mpc. From the 16 galaxy clusters a
weighted mean Hubble Parameter of H0=84.2 +/-6 km s-1 Mpc-1 is found. From the
218 ScI galaxies a Hubble Parameter of H0=83.4 +/-8 km s-1 Mpc-1 is found. When
the zero point of the K-TFR is corrected to account for recent results that
find a Large Magellanic Cloud distance modulus of 18.39 +/-0.05 a Hubble
Parameter of 88.0 +/-6 km s-1 Mpc-1 is found. A comparison with the results of
the Hubble Key Project (Freedman et al 2001) is made and discrepancies between
the K-TFR distances and the HKP I-TFR distances are discussed. Implications for
Lamda-CDM cosmology are considered with H0=84 km s-1 Mpc-1. (Abridged)Comment: 37 pages including 12 tables and 7 figures. Final version accepted
for publication in the Journal of Astrophysics & Astronom
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