Ab initio absorption spectrum of NiO combining molecular dynamics with the embedded cluster approach in a discrete reaction field

We developed a procedure that combines three complementary computational methodologies to improve the theoretical description of the electronic structure of nickel oxide. The starting point is a Car-Parrinello molecular dynamics simulation to incorporate vibrorotational degrees of freedom into the material model. By means ofcomplete active space self-consistent field second-order perturbation theory (CASPT2) calculations on embedded clusters extracted from the resulting trajectory, we describe localized spectroscopic phenomena on NiO with an efficient treatment of electron correlation. The inclusion of thermal motion into the theoretical description allowsus to study electronic transitions that, otherwise, would be dipole forbidden in the ideal structure and results in a natural reproduction of the band broadening. Moreover, we improved the embedded cluster model by incorporating self-consistently at the complete active space self-consistent field (CASSCF) level a discrete (or direct) reaction field (DRF) in the cluster surroundings. The DRF approach offers an efficient treatment ofelectric response effects of the crystalline embedding to the electronic transitions localized in the cluster. We offer accurate theoretical estimates of the absorption spectrum and the density of states around the Fermi level of NiO, and a comprehensive explanation of the source of the broadening and the relaxation of the charge transferstates due to the adaptation of the environment

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Altered structural connectivity in patients with medial temporal lobe epilepsy: A Diffusion Spectrum Imaging and Graph Analysis study

In this study we investigated the effect of medial temporal lobe epilepsy (MTLE) on the global characteristics of brain connectivity estimated by topological measures. We used DSI (Diffusion Spectrum Imaging) to construct a connectivity matrix where the nodes represents the anatomical ROIs and the edges are the connections between any pair of ROIs weighted by the mean GFA/FA values. A significant difference was found between the patient group vs control group in characteristic path length, clustering coefficient and small-worldness. This suggests that the MTLE network is less efficient compared to the network of the control group.

Calculation of Franck-Condon factors including anharmonicity: simulation of the C2 H4+ X̃2B 3u←C2 H4X̃1 Ag band in the photoelectron spectrum of ethylene

Our new simple method for calculating accurate Franck-Condon factors including nondiagonal (i.e., mode-mode) anharmonic coupling is used to simulate the C2H4+X2B 3u←C2H4X̃1 Ag band in the photoelectron spectrum. An improved vibrational basis set truncation algorithm, which permits very efficient computations, is employed. Because the torsional mode is highly anharmonic it is separated from the other modes and treated exactly. All other modes are treated through the second-order perturbation theory. The perturbation-theory corrections are significant and lead to a good agreement with experiment, although the separability assumption for torsion causes the C2 D4 results to be not as good as those for C2 H4. A variational formulation to overcome this circumstance, and deal with large anharmonicities in general, is suggested

Nanomotors: new strategies of (bio)sensing based on motion

Nanomotors are nanoscale devices capable of converting energy into movement and forces. Among them, self-propelled nanomotors offer considerable promise for developing new and novel bioanalytical and biosensing strategies based on the direct isolation of target biomolecules or changes in their movement in the presence of target analytes. The mainachievements of this project consists on the development of receptor-functionalized nanomotors that offer direct and rapid target detection, isolation and transport from raw biological samples without preparatory and washing steps. For example, microtube engines functionalized with aptamer, antibody, lectin and enzymes receptors were used for the direct isolation of analytes of biomedical interest, including proteins and whole cells, among others. A target protein was also isolated from a complex sample by using an antigen-functionalized microengine navigating into the reservoirs of a lab-on-a-chip device. The new nanomotorbased target biomarkers detection strategy not only offers highly sensitive, rapid, simple and low cost alternative for the isolation and transport of target molecules, but also represents a new dimension of analytical information based on motion. The recognition events can be easily visualized by optical microscope (without any sophisticated analytical instrument) to reveal the target presence and concentration. The use of artificial nanomachines has shown not only to be useful for (bio)recognition and (bio)transport but also for detection of environmental contamination and remediation. In this context, micromotors modified with superhydrophobic layer demonstrated that effectively interacted, captured, transported and removed oil droplets from oil contaminated samples. Finally, a unique micromotor-based strategy for water-quality testing, that mimics live-fish water-quality testing, based on changes in the propulsion behavior of artificial biocatalytic microswimmers in the presence of aquatic pollutants was also developed. The attractive features of the new micromachine-based target isolation and signal transduction protocols developed in this project offer numerous potential applications in biomedical diagnostics, environmental monitoring, and forensic analysis.

Understanding the two-component sensing System of virulent bacteria

Report for the scientific sojourn carried out at the l’ Institute for Computational Molecular Science of the Temple University, United States, from 2010 to 2012. Two-component systems (TCS) are used by pathogenic bacteria to sense the environment within a host and activate mechanisms related to virulence and antimicrobial resistance. A prototypical example is the PhoQ/PhoP system, which is the major regulator of virulence in Salmonella. Hence, PhoQ is an attractive target for the design of new antibiotics against foodborne diseases. Inhibition of the PhoQ-mediated bacterial virulence does not result in growth inhibition, presenting less selective pressure for the generation of antibiotic resistance. Moreover, PhoQ is a histidine kinase (HK) and it is absent in animals. Nevertheless, the design of satisfactory HK inhibitors has been proven to be a challenge. To compete with the intracellular ATP concentrations, the affinity of a HK inhibidor must be in the micromolar-nanomolar range, whereas the current lead compounds have at best millimolar affinities. Moreover, the drug selectivity depends on the conformation of a highly variable loop, referred to as the “ATP-lid, which is difficult to study by X-Ray crystallography due to its flexibility. I have investigated the binding of different HK inhibitors to PhoQ. In particular, all-atom molecular dynamics simulations have been combined with enhanced sampling techniques in order to provide structural and dynamic information of the conformation of the ATP-lid. Transient interactions between these drugs and the ATP-lid have been identified and the free energy of the different binding modes has been estimated. The results obtained pinpoint the importance of protein flexibility in the HK-inhibitor binding, and constitute a first step in developing more potent and selective drugs. The computational resources of the hosting institution as well as the experience of the members of the group in drug binding and free energy methods have been crucial to carry out this work.

Sodium sensing in neurons with a dendrimer-based nanoprobe.

Ion imaging is a powerful methodology to assess fundamental biological processes in live cells. The limited efficiency of some ion-sensing probes and their fast leakage from cells are important restrictions to this approach. In this study, we present a novel strategy based on the use of dendrimer nanoparticles to obtain better intracellular retention of fluorescent probes and perform prolonged fluorescence imaging of intracellular ion dynamics. A new sodium-sensitive nanoprobe was generated by encapsulating a sodium dye in a PAMAM dendrimer nanocontainer. This nanoprobe is very stable and has high sodium sensitivity and selectivity. When loaded in neurons in live brain tissue, it homogenously fills the entire cell volume, including small processes, and stays for long durations, with no detectable alterations of cell functional properties. We demonstrate the suitability of this new sodium nanosensor for monitoring physiological sodium responses such as those occurring during neuronal activity.

Induction of innate immune responses through Nalp3 inflammasome sensing of asbestos and silica

The inhalation of airborne pollutants such as asbestos or silica is linked to inflammation of the lung, fibrosis and lung cancer. How the presence of pathogenic dust is recognised, and how chronic inflammatory diseases are triggered are poorly understood. We will se show that asbestos and silica are sensed by the Nalp3 inflammasome, whose subsequent activation leads to IL-1b secretion. Inflammasome activation is triggered by reactive oxygen species, which are generated by a NADPH oxidase upon particle phagocytosis. In a model of asbestos inhalation, Nalp3_/_ mice showed diminished recruitment of inflammatory cells to the lungs, paralleled by lower cytokine production. Our findings implicate the Nalp3 inflammasome in particulate matter-related pulmonary diseases and support its role as a major proinflammatory ''danger" receptor.

Selective regulation of acid-sensing ion channel 1 by serine proteases.

Acid-sensing ion channels (ASICs) are neuronal Na(+) channels that belong to the epithelial Na(+) channel/degenerin family. ASICs are transiently activated by a rapid drop in extracellular pH. Conditions of low extracellular pH, such as ischemia and inflammation in which ASICs are thought to be active, are accompanied by increased protease activity. We show here that serine proteases modulate the function of ASIC1a and ASIC1b but not of ASIC2a and ASIC3. We show that protease exposure shifts the pH dependence of ASIC1a activation and steady-state inactivation to more acidic pH. As a consequence, protease exposure leads to a decrease in current response if ASIC1a is activated by a pH drop from pH 7.4. If, however, acidification occurs from a basal pH of approximately 7, protease-exposed ASIC1a shows higher activity than untreated ASIC1a. We provide evidence that this bi-directional regulation of ASIC1a function also occurs in neurons. Thus, we have identified a mechanism that modulates ASIC function and may allow ASIC1a to adapt its gating to situations of persistent extracellular acidification.

Innate immune activation through Nalp3 inflammasome sensing of asbestos and silica

The inhalation of airborne pollutants, such as asbestos or silica, is linked to inflammation of the lung, fibrosis, and lung cancer. How the presence of pathogenic dust is recognized and how chronic inflammatory diseases are triggered are poorly understood. Here, we show that asbestos and silica are sensed by the Nalp3 inflammasome, whose subsequent activation leads to interleukin-1beta secretion. Inflammasome activation is triggered by reactive oxygen species, which are generated by a NADPH oxidase upon particle phagocytosis. (NADPH is the reduced form of nicotinamide adenine dinucleotide phosphate.) In a model of asbestos inhalation, Nalp3-/- mice showed diminished recruitment of inflammatory cells to the lungs, paralleled by lower cytokine production. Our findings implicate the Nalp3 inflammasome in particulate matter-related pulmonary diseases and support its role as a major proinflammatory "danger" receptor

Reassessing the role of mitochondrial DNA mutations in autism spectrum disorder

Background: There is increasing evidence that impairment of mitochondrial energy metabolism plays an important role in the pathophysiology of autism spectrum disorders (ASD; OMIM number: 209850). A significant proportion of ASD cases display biochemical alterations suggestive of mitochondrial dysfunction and several studies have reported that mutations in the mitochondrial DNA (mtDNA) molecule could be involved in the disease phenotype. Methods: We analysed a cohort of 148 patients with idiopathic ASD for a number of mutations proposed in the literature as pathogenic in ASD. We also carried out a case control association study for the most common European haplogroups (hgs) and their diagnostic single nucleotide polymorphisms (SNPs) by comparing cases with 753 healthy and ethnically matched controls.Results: We did not find statistical support for an association between mtDNA mutations or polymorphisms and ASD.Conclusions: Our results are compatible with the idea that mtDNA mutations are not a relevant cause of ASD and the frequent observation of concomitant mitochondrial dysfunction and ASD could be due to nuclear factors influencing mitochondrion functions or to a more complex interplay between the nucleus and the mitochondrion/mtDNA.

Interplay of spectral efficiency, power and doppler spectrum for reference-signal-assisted wireless communication

Expressions relating spectral efficiency, power, and Doppler spectrum, are derived for Rayleigh-faded wireless channels with Gaussian signal transmission. No side information on the state of the channel is assumed at the receiver. Rather, periodic reference signals are postulated in accordance with the functioning of most wireless systems. The analysis relies on a well-established lower bound, generally tight and asymptotically exact at low SNR. In contrast with most previous studies, which relied on block-fading channel models, a continuous-fading model is adopted. This embeds the Doppler spectrum directly in the derived expressions, imbuing them with practical significance. Closed-form relationships are obtained for the popular Clarke-Jakes spectrum and informative expansions, valid for arbitrary spectra, are found for the low- and high-power regimes. While the paper focuses on scalar channels, the extension to multiantenna settings is also discussed.