237 research outputs found
Modelling Cell Cycle using Different Levels of Representation
Understanding the behaviour of biological systems requires a complex setting
of in vitro and in vivo experiments, which attracts high costs in terms of time
and resources. The use of mathematical models allows researchers to perform
computerised simulations of biological systems, which are called in silico
experiments, to attain important insights and predictions about the system
behaviour with a considerably lower cost. Computer visualisation is an
important part of this approach, since it provides a realistic representation
of the system behaviour. We define a formal methodology to model biological
systems using different levels of representation: a purely formal
representation, which we call molecular level, models the biochemical dynamics
of the system; visualisation-oriented representations, which we call visual
levels, provide views of the biological system at a higher level of
organisation and are equipped with the necessary spatial information to
generate the appropriate visualisation. We choose Spatial CLS, a formal
language belonging to the class of Calculi of Looping Sequences, as the
formalism for modelling all representation levels. We illustrate our approach
using the budding yeast cell cycle as a case study
Enhancing low-rank solutions in semidefinite relaxations of Boolean quadratic problems
Boolean quadratic optimization problems occur in a number of applications.
Their mixed integer-continuous nature is challenging, since it is inherently
NP-hard. For this motivation, semidefinite programming relaxations (SDR's) are
proposed in the literature to approximate the solution, which recasts the
problem into convex optimization. Nevertheless, SDR's do not guarantee the
extraction of the correct binary minimizer. In this paper, we present a novel
approach to enhance the binary solution recovery. The key of the proposed
method is the exploitation of known information on the eigenvalues of the
desired solution. As the proposed approach yields a non-convex program, we
develop and analyze an iterative descent strategy, whose practical
effectiveness is shown via numerical results
Sparse linear regression from perturbed data
The problem of sparse linear regression is relevant in the context of linear
system identification from large datasets. When data are collected from
real-world experiments, measurements are always affected by perturbations or
low-precision representations. However, the problem of sparse linear regression
from fully-perturbed data is scarcely studied in the literature, due to its
mathematical complexity. In this paper, we show that, by assuming bounded
perturbations, this problem can be tackled by solving low-complex l2 and l1
minimization problems. Both theoretical guarantees and numerical results are
illustrated in the paper
Sparse learning with concave regularization: relaxation of the irrepresentable condition
Learning sparse models from data is an important task in all those frameworks where relevant information should be identified within a large dataset. This can be achieved by
formulating and solving suitable sparsity promoting optimization problems. As to linear regression models, Lasso is the most popular convex approach, based on an L1-norm regularization. In contrast, in this paper, we analyse a concave regularized approach, and we prove that it relaxes the irrepresentable condition, which is sufficient and essentially necessary for Lasso to select the right significant parameters. In practice, this has the benefit of reducing the number of necessary measurements with respect to Lasso. Since the proposed problem is nonconvex, we also discuss different algorithms to solve it, and we illustrate the obtained enhancement via numerical experiments
The platform switching approach to optimize split crest technique.
The split crest technique is a reliable procedure used simultaneously in the implant positioning. In the literature some authors describe a secondary bone resorption as postoperative complication. The authors show how platform switching can be able to avoid secondary resorption as complication of split crest technique
Multidisciplinary Approach to Fused Maxillary central Incisors: a Case Report
Introduction: The fusion of permanent teeth is a development anomaly of dental hard tissue. It may require a hard
multidisciplinary approach with orthodontics, endodontics, surgery and prosthetics to solve aesthetic and functional
problems.
Case presentation: A 20-year-old Caucasian man presented to our Department to solve a dental anomaly of his
upper central incisors. An oral investigation revealed the fusion of his maxillary central incisors and dyschromia of
right central incisor. Vitality pulp tests were negative for lateral upper incisors and left central incisor. Radiographic
examinations showed a fused tooth with two separate pulp chambers, two distinct roots and two separate root
canals. There were also periapical lesions of central incisors and right lateral incisor, so he underwent endodontic
treatment. Six months later, OPT examination revealed persistence of the periapical radiolucency, so endodontic
surgery was performed, which included exeresis of the lesion, an apicoectomy and retrograde obturation with a
reinforced zinc oxide-eugenol cement (SuperEBA) Complete healing of the lesion was obtained six months
postoperatively. Fused teeth crowns were separated and orthodontic appliances were put in place. When correct
teeth position was achieved (after nine months), the anterior teeth were prosthetically rehabilitated.
Conclusion: Many treatment options have been proposed in the literature to solve cases of dental fusion. The best
treatment plan depends on the nature of the anomaly, its location, the morphology of the pulp chamber
and root canal system, the subgingival extent of the separation line, and the patient compliance. Following
an analysis of radiographical and clinical data, it was possible to solve our patient’s dental anomaly with a
multidisciplinary approach
Specifying and Verifying Concurrent Algorithms with Histories and Subjectivity
We present a lightweight approach to Hoare-style specifications for
fine-grained concurrency, based on a notion of time-stamped histories that
abstractly capture atomic changes in the program state. Our key observation is
that histories form a partial commutative monoid, a structure fundamental for
representation of concurrent resources. This insight provides us with a
unifying mechanism that allows us to treat histories just like heaps in
separation logic. For example, both are subject to the same assertion logic and
inference rules (e.g., the frame rule). Moreover, the notion of ownership
transfer, which usually applies to heaps, has an equivalent in histories. It
can be used to formally represent helping---an important design pattern for
concurrent algorithms whereby one thread can execute code on behalf of another.
Specifications in terms of histories naturally abstract granularity, in the
sense that sophisticated fine-grained algorithms can be given the same
specifications as their simplified coarse-grained counterparts, making them
equally convenient for client-side reasoning. We illustrate our approach on a
number of examples and validate all of them in Coq.Comment: 17 page
Fixed-order FIR approximation of linear systems from quantized input and output data
Abstract The problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order FIR model providing the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example
Changes in the distribution of muscle activity when using a passive trunk exoskeleton depend on the type of working task: A high-density surface EMG study
Exoskeleton effectiveness in reducing muscle efforts has been usually assessed from surface electromyograms (EMGs) collected locally. It has been demonstrated, however, muscle activity redistributes within the low back muscles during static and dynamic contractions, suggesting the need of detecting surface EMGs from a large muscle region to reliably investigate changes in global muscle activation. This study used high-density surface EMG to assess the effects of a passive trunk exoskeleton on the distribution of low back muscles’ activity during different working tasks. Ten, male volunteers performed a static and a dynamic task with and without the exoskeleton. Multiple EMGs were sampled bilaterally from the lumbar erector spinae muscles while the hip and knee angles were measured unilaterally. Key results revealed for the static task exoskeleton led to a decrease in the average root mean square (RMS) amplitude (∼10%) concomitantly with a stable mean frequency and a redistribution of muscle activity (∼0.5 cm) in the caudal direction toward the end of the task. For the dynamic task, the exoskeleton reduced the RMS amplitude (∼5%) at the beginning of the task and the variability in the muscle activity distribution during the task. Moreover, a reduced range of motion in the lower limb was observed when using the exoskeleton during the dynamic task. Current results support the notion the passive exoskeleton has the potential to alleviate muscular loading at low back level especially for the static task
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