The frequent instability of mandibular removable complete dentures affects patient Oral Health Related Quality of Life (OHRQoL). An innovative therapeutic strategy used to improve stability involves placing four symphyseal mini-implants. This study was aimed at assessing OHRQoL over time in subjects in which mini-implants were placed and exploring if certain parameters could predict the evolution of their OHRQoL. The OHRQoL of subjects with dentures was assessed using the Geriatric Oral Health Assessment Index (GOHAI) before (T0), 2-6 months (T1), twelve months (T2) and twenty-four or more months (T3) after mini-implant setting. Age, gender and chewing ability were tested as explanatory variables for the change in OHRQoL with time. Thirteen women and six men were included (mean age: 69 ± 10 years). After treatment, mean GOHAI scores at T1, T2 and T3 increased significantly (p < 0.001). The GOHAI-Add mean score was not affected by age or gender. Baseline chewing ability impacted the "functional" and "pain and discomfort" fields of the mean GOHAI scores (p < 0.05). The OHRQoL quickly improved after mini-implant placement in complete denture wearers and then stabilized over time. Baseline chewing ability can be used as a predictive parameter of OHRQoL.
We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.
- Journal of physics. Condensed matter : an Institute of Physics journal
- Published almost 6 years ago
We present a technique for analyzing the full three-dimensional density profiles of planar crystal-fluid interfaces in terms of density modes. These density modes can also be related to crystallinity order parameter profiles which are used in coarse-grained, phase field type models of the statics and dynamics of crystal-fluid interfaces and are an alternative to crystallinity order parameters extracted from simulations using local crystallinity criteria. We illustrate our results for the hard sphere system using finely resolved, three-dimensional density profiles from a density functional theory of fundamental measure type.
- IEEE transactions on pattern analysis and machine intelligence
- Published about 3 years ago
Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce.We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function that measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. Label embedding enjoys a built-in ability to leverage alternative sources of information instead of or in addition to attributes, such as e.g. class hierarchies or textual descriptions. Moreover, label embedding encompasses the whole range of learning settings from zero-shot learning to regular learning with a large number of labeled examples.
The determination of solubility parameters for solutes represents a challenging mathematical problem of locating the central tendency of solvent affinity based on a limited set of data taken from experimental observations. At present, the most commonly used methods for computing solubility parameters of a solute require a binary classification of solvent affinity for the solute and employ a spherical/ellipsoidal compatibility region in the three-dimensional Hansen solubility parameter (δD, δP, δH) space. Utilizing a binary classification requires an arbitrary solubility threshold, and an ellipsoidal fitting model imposes a symmetry on the intermolecular forces that is rarely reflected by the experimental data. To overcome these issues, an approach that makes use of accurate solubility data to describe a three-dimensional solubility function, f, is introduced. The principles of the approach are discussed in detail and the procedures for constructing the solubility function and computing solubility parameters are described. An example using PCBM solubility data available in the literature demonstrates the new method. Lastly, a method that employs f as a predictor of solubility in arbitrary solvents with a proposed measure of reliability is presented.
- The Journal of neuroscience : the official journal of the Society for Neuroscience
- Published about 3 years ago
The relationship between muscle activity and behavioral output determines how the brain controls and modifies complex skills. In vocal control, ensembles of muscles are used to precisely tune single acoustic parameters such as fundamental frequency and sound amplitude. If individual vocal muscles were dedicated to the control of single parameters, then the brain could control each parameter independently by modulating the appropriate muscle or muscles. Alternatively, if each muscle influenced multiple parameters, a more complex control strategy would be required to selectively modulate a single parameter. Additionally, it is unknown whether the function of single muscles is fixed or varies across different vocal gestures. A fixed relationship would allow the brain to use the same changes in muscle activation to, for example, increase the fundamental frequency of different vocal gestures, whereas a context-dependent scheme would require the brain to calculate different motor modifications in each case. We tested the hypothesis that single muscles control multiple acoustic parameters and that the function of single muscles varies across gestures using three complementary approaches. First, we recorded electromyographic data from vocal muscles in singing Bengalese finches. Second, we electrically perturbed the activity of single muscles during song. Third, we developed an ex vivo technique to analyze the biomechanical and acoustic consequences of single-muscle perturbations. We found that single muscles drive changes in multiple parameters and that the function of single muscles differs across vocal gestures, suggesting that the brain uses a complex, gesture-dependent control scheme to regulate vocal output.
Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks.
Baseline anterior segment imaging parameters associated with incident gonioscopic angle closure, to our knowledge, are unknown.
We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on. The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of 183 European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios. We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall stability of the system increases between 2008 and 2013.
Estimates of life-history parameters were made for shark-like batoids of conservation concern Rhynchobatus spp. (Rhynchobatus australiae, Rhynchobatus laevis and Rhynchobatus palpebratus) and Glaucostegus typus using vertebral ageing. The sigmoid growth functions, Gompertz and logistic, best described the growth of Rhynchobatus spp. and G. typus, providing the best statistical fit and most biologically appropriate parameters. The two-parameter logistic was the preferred model for Rhynchobatus spp. with growth parameter estimates (both sexes combined) L∞ = 2045 mm stretch total length, LST and k = 0·41 year(-1) . The same model was also preferred for G. typus with growth parameter estimates (both sexes combined) L∞ = 2770 mm LST and k = 0·30 year(-1) . Annual growth-band deposition could not be excluded in Rhynchobatus spp. using mark-recaptured individuals. Although morphologically similar G. typus and Rhynchobatus spp. have differing life histories, with G. typus longer lived, slower growing and attaining a larger maximum size.