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Register a new userDe novo topology optimization of Total Ossicular Replacement Prostheses
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Conductive hearing loss, due to middle ear pathologies or traumas, affects more than 5% of the population worldwide. Passive prostheses to replace the ossicular chain mainly rely on piston-like titanium and/or hydroxyapatite devices, which in the long term suffer from extrusion. Although the basic shape of such devices always consists of a base for contact with the eardrum and a stem to have mechanical connection with the residual bony structures, a plethora of topologies have been proposed, mainly to help surgical positioning. In this work, we optimize the topology of a total ossicular replacement prosthesis, by maximizing the global stiffness and under the smallest possible volume constraint that ensures material continuity. This investigation optimizes the prosthesis topology in response to static displacement loads with amplitudes that normally occur during sound stimulation in a frequency range between 100 Hz and 10 kHz. Following earlier studies, we discuss how the presence and arrangement of holes on the surface of the prosthesis plate in contact with the umbo affect the overall geometry. Finally, we validate the designs through a finite-element model, in which we assess the prosthesis performance upon dynamic sound pressure loads by considering four different constitutive materials: titanium, cortical bone, silk, and collagen/hydroxyapatite. The results show that the selected prostheses present, almost independently of their constitutive material, a vibroacustic behavior close to that of the native ossicular chain, with a slight almost constant positive shift that reaches a maximum of 5 dB close to 1 kHz. This work represents a reference for the development of a new generation of middle ear prostheses with non-conventional topologies for fabrication via additive manufacturing technologies or ultraprecision machining in order to create patient-specific devices.
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A multiscale mathematical model is presented to describe the de novo granulation and the evolution of multispecies granular biofilms within a continuous reactor. The granule is modelled as a spherical free boundary domain with radial symmetry. The equation which governs the free boundary is derived from global mass balance considerations and takes into account the growth of sessile biomass and the exchange fluxes with the bulk liquid. Starting from a vanishing initial value, the expansion of the free boundary is initiated by the attachment process, which depends on the microbial species concentrations within the bulk liquid and their specific attachment velocity. Nonlinear hyperbolic PDEs model the growth of the sessile microbial species, while quasi-linear parabolic PDEs govern the dynamics of substrates and invading species within the granular biofilm. Nonlinear ODEs govern the evolution of soluble substrates and planktonic biomass within the bulk liquid. The model is applied to an anaerobic granular-based system and solved numerically to test its qualitative behaviour and explore the main aspects of de novo anaerobic granulation: ecology, biomass distribution, relative abundance, dimensional evolution of the granules and soluble substrates and planktonic biomass dynamics within the reactor. The numerical results confirm that the model accurately describes the ecology and the concentrically-layered structure of anaerobic granules observed experimentally, and is able to predict the effects of some significant factors, such as influent wastewater composition, granulation properties of planktonic biomass, biomass density and hydrodynamic and shear stress conditions, on the process performance.
RNA is a fundamental class of biomolecules that mediate a large variety of molecular processes within the cell. Computational algorithms can be of great help in the understanding of RNA structure-function relationship. One of the main challenges in this field is the development of structure-prediction algorithms, which aim at the prediction of the three-dimensional (3D) native fold from the sole knowledge of the sequence. In a recent paper, we have introduced a scoring function for RNA structure prediction. Here, we analyze in detail the performance of the method, we underline strengths and shortcomings, and we discuss the results with respect to state-of-the-art techniques. These observations provide a starting point for improving current methodologies, thus paving the way to the advances of more accurate approaches for RNA 3D structure prediction.
Contrary to long-held views, recent evidence indicates that $textit{de novo}$ birth of genes is not only possible, but is surprisingly prevalent: a substantial fraction of eukaryotic genomes are composed of orphan genes, which show no homology with any conserved genes. And a remarkably large proportion of orphan genes likely originated $textit{de novo}$ from non-genic regions. Here, using a parsimonious mathematical model, we investigate the probability and timescale of $textit{de novo}$ gene birth due to spontaneous mutations. We trace how an initially non-genic locus accumulates beneficial mutations to become a gene. We sample across a wide range of biologically feasible distributions of fitness effects (DFE) of mutations, and calculate the conditions conducive to gene birth. We find that in a time frame of millions of years, gene birth is highly likely for a wide range of DFEs. Moreover, when we allow DFEs to fluctuate, which is expected given the long time frame, gene birth in the model becomes practically inevitable. This supports the idea that gene birth is a ubiquitous process, and should occur in a wide variety of organisms. Our results also demonstrate that intergenic regions are not inactive and silent but are more like dynamic storehouses of potential genes.
Depolarization of circularly polarized light scattered from biological tissues depends on structural changes in cell nuclei, which can provide valuable information for differentiating cancer tissues concealed in healthy tissues. In this study, we experimentally verified the possibility of cancer identification using scattering of circularly polarized light. We investigated the polarization of light scattered from a sliced biological tissue with various optical configurations. A significant difference between circular polarizations of light scattered from cancerous and healthy tissues is observed, which is sufficient to distinguish a cancerous region. The line-scanning experiments along a region incorporating healthy and cancerous parts indicate step-like behaviors in the degree of circular polarization corresponding to the state of tissues, whether cancerous or normal. An oblique and perpendicular incidence induces different resolutions for identifying cancerous tissues, which indicates that the optical arrangement can be selected according to the priority of resolution.
De novo whole genome assembly reconstructs genomic sequence from short, overlapping, and potentially erroneous DNA segments and is one of the most important computations in modern genomics. This work presents HipMER, a high-quality end-to-end de novo assembler designed for extreme scale analysis, via efficient parallelization of the Meraculous code. Genome assembly software has many components, each of which stresses different components of a computer system. This chapter explains the computational challenges involved in each step of the HipMer pipeline, the key distributed data structures, and communication costs in detail. We present performance results of assembling the human genome and the large hexaploid wheat genome on large supercomputers up to tens of thousands of cores.
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