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 about 5 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 2 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 Journal of neuroscience : the official journal of the Society for Neuroscience
- Published about 2 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.
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.
We are overwhelmed by experimental data, and need better ways to understand large interaction datasets. While clustering related nodes in such networks-known as community detection-appears a promising approach, detecting such communities is computationally difficult. Further, how to best use such community information has not been determined. Here, within the context of protein function prediction, we address both issues. First, we apply a novel method that generates improved modularity solutions than the current state of the art. Second, we develop a better method to use this community information to predict proteins' functions. We discuss when and why this community information is important. Our results should be useful for two distinct scientific communities: first, those using various cost functions to detect community structure, where our new optimization approach will improve solutions, and second, those working to extract novel functional information about individual nodes from large interaction datasets.
Micromotors are important for a wide variety of applications. Here, we develop a microfluidic approach for one-step fabrication of a Janus self-propelled micromotor with multiple functions. By fine tuning the fabrication parameters and loading functional nanoparticles, our micromotor reaches a high speed and achieves an oriented function to promote the water purification efficiency and recycling process.