A Logical Descriptor for Regular Languages via Stone Duality

In this paper we introduce a class of descriptors for regular languages arising from an application of the Stone duality between finite Boolean algebras and finite sets. These descriptors, called classical fortresses, are object specified in classical propositional logic and capable to accept exactly regular languages. To prove this, we show that the languages accepted by classical fortresses and deterministic finite automata coincide. Classical fortresses, besides being propositional descriptors for regular languages, also turn out to be an efficient tool for providing alternative and intuitive proofs for the closure properties of regular languages.

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Extrinsic causes of erosion. Biological factors

Biological factors such as saliva, acquired dental pellicle, tooth structure and positioning in relation to soft tissues and tongue are related to dental erosion development. Saliva has been shown to be the most important biological factor in the prevention of dental erosion. It starts acting even before the acid attack, with the increase of the salivary flow rate as a response to the acidic stimuli. This creates a favorable scenario, increasing the buffering system of saliva and effectively diluting and clearing acids on dental surfaces during the erosive challenge. Saliva plays a role in the formation of the acquired dental pellicle, which acts as a perm-selective membrane preventing contact of the acid with the tooth surf aces. The protective level of the pellicle seems to be regulated by its composition, thickness and maturation time. Due to its mineral content, saliva can also prevent demineralization as well as enhance remineralization. However, these preventive and reparative factors of saliva may not be enough against highly erosive challenges, leading to erosion development. The progress rate of erosion can be significantly influenced by the type of dental substrate, occurrence of mechanical and chemical attacks, fluoride exposure, and also by contact with the oral soft tissues and tongue.

Brain electrical microstates in subjects with panic disorder

Brain electrical microstates represent spatial configurations of scalp recorded brain electrical activity and are considered to be the basic elements of stepwise processing of information in the brain. In the present study, the hypothesis of a temporo-limbic dysfunction in panic disorder (PD) was tested by investigating the topographic descriptors of brain microstates, in particular the one corresponding to the Late Positive Complex (LPC), an event-related potential (ERP) component with generators in these regions. ERPs were recorded in PD patients and matched healthy subjects during a target detection task, in a central (CC) and a lateral condition (LC). In the CC, a leftward shift of the LPC microstate positive centroid was observed in the patients with PD versus the healthy control subjects. In the LC, the topographic descriptor of the first microstate showed a rightward shift, while those of both the second and the fourth microstate, corresponding to the LPC, revealed a leftward shift in the PD patients versus the healthy control subjects. These findings indicate an overactivation of the right hemisphere networks involved in early visual processing and a hypoactivation of the right hemisphere circuits involved in LPC generators in PD. In line with this interpretation, the abnormal topography of the LPC microstate, observed in the CC, was associated with a worse performance on a test exploring right temporo-hippocampal functioning. Topographical abnormalities found for the LPC microstate in the LC were associated with a higher number of panic attacks, suggesting a pathogenetic role of the right temporo-hippocampal dysfunction in PD.

Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

The Chemical Space of Flavours

The flavour of foods is determined by the interaction of taste molecules with receptors in the mouth, and fragrances or aroma with receptors in the upper part of the nose. Here, we discuss the properties of taste and fragrance molecules, from the public databases Superscent, Flavornet, SuperSweet and BitterDB, taken collectively as flavours, in the perspective of the chemical space. We survey simple descriptor profiles in comparison with the public collections ChEMBL (bioactive small molecules), ZINC (commercial drug-like molecules) and GDB-13 (all possible organic molecules up to 13 atoms of C, N, O, S, Cl). A global analysis of the chemical space of flavours is also presented based on molecular quantum numbers (MQN) and SMILES fingerprints (SMIfp). While taste molecules span a very broad property range, fragrances occupy a narrow area of the chemical space consisting of generally very small and relatively nonpolar molecules distinct of standard drug molecules. Proximity searching in the chemical space is exemplified as a simple method to facilitate the search for new fragrances.