Coarse-graining the vertex model and its response to shear

Tissue dynamics and collective cell motion are crucial biological processes. Their biological machinery is mostly known, and simulation models such as the "active vertex model" (AVM) exist and yield reasonable agreement with experimental observations like tissue fluidization or fingering. However, a good and well-founded continuum description for tissues remains to be developed. In this work we derive a macroscopic description for a two-dimensional cell monolayer by coarse-graining the vertex model through the Poisson bracket approach. We obtain equations for cell density, velocity and the cellular shape tensor. We then study the homogeneous steady states, their stability (which coincides with thermodynamic stability), and especially their behavior under an externally applied shear. Our results contribute to elucidate the interplay between flow and cellular shape. The obtained macroscopic equations present a good starting point for adding cell motion, morphogenetic and other biologically relevant processes.Comment: 14 pages, 11 figure

Improving the predictive quality of time‐dependent density functional theory calculations of the X‐ray emission spectroscopy of organic molecules

The simulation of x-ray emission spectra of organic molecules using time-dependent density functional theory (TDDFT) is explored. TDDFT calculations using standard hybrid exchange-correlation functionals in conjunction with large basis sets can predict accurate X-ray emission spectra provided an energy shift is applied to align the spectra with experiment. The relaxation of the orbitals in the intermediate state is an important factor, and neglect of this relaxation leads to considerably poorer predicted spectra. A short-range corrected functional is found to give emission energies that required a relatively small energy shift to align with experiment. However, increasing the amount of Hartree-Fock exchange in this functional to remove the need for any energy shift led to a deterioration in the quality of the calculated spectral profile. To predict accurate spectra without reference to experimental measurements, we use the CAM-B3LYP functional with the energy scale determined with reference to a ∆self-consistent field (SCF) calculation for the highest energy emission transition

Self-Association of Organic Solutes in Solution: A NEXAFS Study of Aqueous Imidazole

N K-edge near-edge X-ray absorption fine-structure (NEXAFS) spectra of imidazole in concentrated aqueous solutions have been acquired. The NEXAFS spectra of the solution species differ significantly from those of imidazole monomers in the gas phase and in the solid state of imidazole, demonstrating the strong sensitivity of NEXAFS to the local chemical and structural environment. In a concentration range from 0.5 to 8.2 mol L−1 the NEXAFS spectrum of aqueous imidazole does not change strongly, confirming previous suggestions that imidazole self-associates are already present at concentrations more dilute than the range investigated here. We show that various types of electronic structure calculations (Gaussian, StoBe, CASTEP) provide a consistent and complete interpretation of all features in the gas phase and solid state spectra based on ground state electronic structure. This suggests that such computational modelling of experimental NEXAFS will permit an incisive analysis of the molecular interactions of organic solutes in solutions. It is confirmed that microhydrated clusters with a single imidazole molecule are poor models of imidazole in aqueous solution. Our analysis indicates that models including both a hydrogen-bonded network of hydrate molecules, and imidazole–imidazole interactions, are necessary to explain the electronic structure evident in the NEXAFS spectra

An ant colony-based semi-supervised approach for learning classification rules

Semi-supervised learning methods create models from a few labeled instances and a great number of unlabeled instances. They appear as a good option in scenarios where there is a lot of unlabeled data and the process of labeling instances is expensive, such as those where most Web applications stand. This paper proposes a semi-supervised self-training algorithm called Ant-Labeler. Self-training algorithms take advantage of supervised learning algorithms to iteratively learn a model from the labeled instances and then use this model to classify unlabeled instances. The instances that receive labels with high confidence are moved from the unlabeled to the labeled set, and this process is repeated until a stopping criteria is met, such as labeling all unlabeled instances. Ant-Labeler uses an ACO algorithm as the supervised learning method in the self-training procedure to generate interpretable rule-based models—used as an ensemble to ensure accurate predictions. The pheromone matrix is reused across different executions of the ACO algorithm to avoid rebuilding the models from scratch every time the labeled set is updated. Results showed that the proposed algorithm obtains better predictive accuracy than three state-of-the-art algorithms in roughly half of the datasets on which it was tested, and the smaller the number of labeled instances, the better the Ant-Labeler performance

Resonant Lifetime of Core-Excited Organic Adsorbates from First Principles

We investigate by first-principles simulations the resonant electron-transfer lifetime from the excited state of an organic adsorbate to a semiconductor surface, namely isonicotinic acid on rutile TiO$_2$(110). The molecule-substrate interaction is described using density functional theory, while the effect of a truly semi-infinite substrate is taken into account by Green's function techniques. Excitonic effects due to the presence of core-excited atoms in the molecule are shown to be instrumental to understand the electron-transfer times measured using the so-called core-hole-clock technique. In particular, for the isonicotinic acid on TiO$_2$(110), we find that the charge injection from the LUMO is quenched since this state lies within the substrate band gap. We compute the resonant charge-transfer times from LUMO+1 and LUMO+2, and systematically investigate the dependence of the elastic lifetimes of these states on the alignment among adsorbate and substrate states.Comment: 24 pages, 6 figures, to appear in Journal of Physical Chemistry

Self-labeling techniques for semi-supervised time series classification: an empirical study

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context

The relationship between natural outdoor environments and cognitive functioning and its mediators

Background Urban residents may experience cognitive fatigue and little opportunity for mental restoration due to a lack of access to nature. Natural outdoor environments (NOE) are thought to be beneficial for cognitive functioning, but underlying mechanisms are not clear. Objectives To investigate the long-term association between NOE and cognitive function, and its potential mediators. Methods This cross-sectional study was based on adult participants of the Positive Health Effects of the Natural Outdoor Environment in Typical Populations in Different Regions in Europe (PHENOTYPE) project. Data were collected in Barcelona, Spain; Doetinchem, the Netherlands; and Stoke-on-Trent, United Kingdom. We assessed residential distance to NOE, residential surrounding greenness, perceived amount of neighborhood NOE, and engagement with NOE. Cognitive function was assessed with the Color Trails Test (CTT). Mediation analysis was undertaken following Baron and Kenny. Results Each 100 m increase in residential distance to NOE was associated with a longer CTT completion time of 1.50% (95% CI 0.13, 2.89). No associations were found for other NOE indicators and cognitive function. Neighborhood social cohesion was (marginally) significantly associated with both residential distance to NOE and CTT completion time, but no evidence for mediation was found. Nor were there indications for mediation by physical activity, social interaction with neighbors, loneliness, mental health, air pollution worries, or noise annoyance. Conclusions Our findings provide some indication that proximity to nature may benefit cognitive function. We could not establish which mechanisms may explain this relationship

Education can improve the negative perception of a threatened long-lived scavenging bird, the Andean condor

Human-wildlife conflicts currently represent one of the main conservation problems for wildlife species around the world. Vultures have serious conservation concerns, many of which are related to people's adverse perception about them due to the belief that they prey on livestock. Our aim was to assess local perception and the factors influencing people's perception of the largest scavenging bird in South America, the Andean condor. For this, we interviewed 112 people from Valle Fértil, San Juan province, a rural area of central west Argentina. Overall, people in the area mostly have an elementary education, and their most important activity is livestock rearing. The results showed that, in general, most people perceive the Andean condor as an injurious species and, in fact, some people recognize that they still kill condors. We identified two major factors that affect this perception, the education level of villagers and their relationship with livestock ranching. Our study suggests that conservation of condors and other similar scavengers depends on education programs designed to change the negative perception people have about them. Such programs should be particularly focused on ranchers since they are the ones who have the worst perception of these scavengers. We suggest that highlighting the central ecological role of scavengers and recovering their cultural value would be fundamental to reverse their persecution and their negative perception by people.Fil: Cailly Arnulphi, Verónica Beatríz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; ArgentinaFil: Lambertucci, Sergio Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; ArgentinaFil: Borghi, Carlos Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Centro de Investigaciones de la Geosfera y Biosfera. Universidad Nacional de San Juan. Facultad de Ciencias Exactas Físicas y Naturales. Centro de Investigaciones de la Geosfera y Biosfera; Argentin

Instance reduction for one-class classification

Instance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in multi-class problems providing very competitive results. However, this issue was rarely addressed in the context of one-class classification. In this specific domain a reduction of the training set may not only decrease the classification time and classifier’s complexity, but also allows us to handle internal noisy data and simplify the data description boundary. We propose two methods for achieving this goal. The first one is a flexible framework that adjusts any instance reduction method to one-class scenario by introduction of meaningful artificial outliers. The second one is a novel modification of evolutionary instance reduction technique that is based on differential evolution and uses consistency measure for model evaluation in filter or wrapper modes. It is a powerful native one-class solution that does not require an access to counterexamples. Both of the proposed algorithms can be applied to any type of one-class classifier. On the basis of extensive computational experiments, we show that the proposed methods are highly efficient techniques to reduce the complexity and improve the classification performance in one-class scenarios