Bayesian Networks Analysis of Malocclusion Data

In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expansion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment

Bayesian Networks Analysis of Malocclusion Data

In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expansion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment

Citizen Hariri and neoliberal politics in postwar Lebanon

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An in vitro study of the interaction of Sea-Nine with rat lever mitochondria

The interactions of the antifouling compound Sea-Ninetwith rat liver mitochondria have been studied. The results indicate that low doses of this compound inhibit adenosine 59-triphosphate (ATP) synthesis. Further investigations indicate that ATP synthesis inhibition should be due to an interaction of Sea-Nine with the succinic dehydrogenase in the mitochondrial respiratory chain

A new procedure for the monitoring of Cationic Detergents in solution

The paper describes a new procedure for the selective monitoring of cationic surfactants in solution. The procedure is based on the fact that cationic surfactants are accumulated inside mitochondria by a potential-driven mechanism. Once inside, the surfactant induces the release of the dye Safranine, previously accumulated inside mitochondria. Therefore the monitoring consists of a direct spectrophotometric measure of the rate of release of safranine in the resuspending medium containing the cationic surfactant

A possible transport mechanism for aluminum in biological membranes

The transport mechanism of aluminum in lysosomes extracted from rat liver has been investigated in this paper. The experi- mental evidence supports the hypothesis that aluminum is transported inside lysosomes in the form of an Al(OH)3 electroneutral compound, the driving force being the internal acidic pH. This mechanism could help to explain the presence of aluminum in cells in many illnesses

Nodular Thyroid Disease in the Era of Precision Medicine

Management of thyroid nodules in the era of precision medicine is continuously changing. Neck ultrasound plays a pivotal role in the diagnosis and several ultrasound stratification systems have been proposed in order to predict malignancy and help clinicians in therapeutic and follow-up decision. Ultrasound elastosonography is another powerful diagnostic technique and can be an added value to stratify the risk of malignancy of thyroid nodules. Moreover, the development of new techniques in the era of "Deep Learning," has led to a creation of machine-learning algorithms based on ultrasound examinations that showed similar accuracy to that obtained by expert radiologists. Despite new technologies in thyroid imaging, diagnostic surgery in 50-70% of patients with indeterminate cytology is still performed. Molecular tests can increase accuracy in diagnosis when performed on "indeterminate" nodules. However, the more updated tools that can be used to this purpose in order to "rule out" (Afirma GSC) or "rule in" (Thyroseq v3) malignancy, have a main limitation: the high costs. In the last years various image-guided procedures have been proposed as alternative and less invasive approaches to surgery for symptomatic thyroid nodules. These minimally invasive techniques (laser and radio-frequency ablation, high intensity focused ultrasound and percutaneous microwave ablation) results in nodule shrinkage and improvement of local symptoms, with a lower risk of complications and minor costs compared to surgery. Finally, ultrasound-guided ablation therapy was introduced with promising results as a feasible treatment for low-risk papillary thyroid microcarcinoma or cervical lymph node metastases

Uniform random generation of large acyclic digraphs

Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering of gene regulatory networks, not only the estimation of model parameters but the reconstruction of the structure itself is of great interest. As well as for the assessment of different structure learning algorithms in simulation studies, a uniform sample from the space of directed acyclic graphs is required to evaluate the prevalence of certain structural features. Here we analyse how to sample acyclic digraphs uniformly at random through recursive enumeration, an approach previously thought too computationally involved. Based on complexity considerations, we discuss in particular how the enumeration directly provides an exact method, which avoids the convergence issues of the alternative Markov chain methods and is actually computationally much faster. The limiting behaviour of the distribution of acyclic digraphs then allows us to sample arbitrarily large graphs. Building on the ideas of recursive enumeration based sampling we also introduce a novel hybrid Markov chain with much faster convergence than current alternatives while still being easy to adapt to various restrictions. Finally we discuss how to include such restrictions in the combinatorial enumeration and the new hybrid Markov chain method for efficient uniform sampling of the corresponding graphs.Comment: 15 pages, 2 figures. To appear in Statistics and Computin