Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Downstream processing development of enveloped viruses for clinical applications: innovative tools for rational process optimization

Dissertation presented to obtain a Ph.D. degree in Engineering and Technology Sciences, Biotechnology at the Instituto de Tecnologia Química e Biológica, Universidade Nova de LisboaViral vectors and virus-like particles hold a tremendous potential in various clinical applications in the areas of gene therapy and/or vaccination, drawing the attention of biotechnology and pharmaceutical companies. The majority of these products are manufactured in animal cell cultures, inherently making the process costly. A great deal of effort is taking place to generate optimized biological and engineering strategies to find scalable and cost-effective processes, easily transferable to cGMP facilities. However, the implementation of robust downstream processes generating this type of biopharmaceuticals in the amounts required for pre-clinical and clinical trials is still lacking and lagging. By including a labile lipid membrane layer harboring glycoproteins (often critical for infection) over the viral capsid, enveloped viruses bring extra challenges in terms of their bioprocessing particularly downstream. The work developed during this thesis aimed at improving the state-of-the-art purification processes for these types of viral particles. The rationale was to integrate process understanding with product characterization, still scarce in such biological systems.(...

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Structural characterization of Arabidopsis thaliana ethylene signaling molecules and the non-ribosomal peptide synthetase from Planktothrix agardhii

Plants employ a complex network of signaling pathways to regulate developmental processes and to mediate the responses to both environmental and biological stress factors. Ethylene is one of the key plant hormones involved in controlling this network, which has made it and its signaling pathway a target of intense research for several decades. In the model plant Arabidopsis thaliana, the plant hormone is detected by a group of five receptors (ETR1, ERS1, ETR2, ERS2 and EIN4) that resemble the sensor histidine kinases of bacterial two-component system. The main aim in this thesis study was the expression and purification of the full-length ETR1 for structural studies to gain insights into the initial steps in ethylene signaling. The FL ETR1 was successfully expressed in baculovirus expression vector system but the isolation of the receptor from the membrane was hampered. In addition to the FL ETR1, the cytosolic portion of the receptor was studied using Small Angle X-ray Scattering. The resulting SAXS model had the expected dimeric arrangement. EDR1 from A. thaliana is a CTR1-like MAPKKK that is involved in regulating disease resistance responses, cell death and also ethylene-induced senescence. It possesses an N-terminal regulatory domain and C-terminal catalytic domain wit Ser/Thr kinase activity. As EDR1 has been shown to autophosphorylate in trans, the mechanism of this was studied using X-ray crystallography. A crystal structure for the catalytically inactive kinase domain of EDR1 (EDR1-D792N) was obtained in the presence of the ATP substrate analog AMP-PNP. The asymmetric unit contained two molecules, one of which surprisingly was in an active-like conformation. Furthermore, the active-like EDR1-D792N molecule was found to form an authentic trans-autophosphorylation complex with the inactive monomer from the adjacent asymmetric unit. In addition to the plant defense signaling proteins, an adenylation (A) domain from cyanobacterial non-ribosomal peptide synthetase (NRPS) was studied. NRPSs are large multidomain enzymes that are found from a number of fungal and bacterial species and catalyze the ribosome-independent assembly of biologically active peptides with diverse composition and function. The A domain plays a central role in the NRPS system as it recognizes and activates the amino acid, which is incorporated into the growing peptide. The A domain ApnA A1 from the Anabaenopeptin synthetase of Planktothrix agardhii is an interesting member of its class as it has an unusual ability to activate two very distinct amino acids (arginine and tyrosine). Structural studies on this enzyme were performed to elucidate its bi-specificity. Based on the solved ApnA A1 structures, two active site residues with a crucial role in the substrate binding were identified. The mutation of these residues led to enzyme variants, which were mono-specific for either tyrosine or arginine, or in some instances were able to activate L-tryptophan. Additionally a number of ApnA A1 mutants were shown to activate unnatural amino acids (4-fluorophenylalanine and 4-azidophenylalanine). A final peptide product with an unnatural amino acid incorporated, could possibly have useful industrial or pharmaceutical applications

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

Hyperspectral imaging acquires data in both the spatial and frequency domains to offer abundant physical or biological information. However, conventional hyperspectral imaging has intrinsic limitations of bulky instruments, slow data acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral learning for snapshot hyperspectral imaging in which sampled hyperspectral data in a small subarea are incorporated into a learning algorithm to recover the hypercube. Hyperspectral learning exploits the idea that a photograph is more than merely a picture and contains detailed spectral information. A small sampling of hyperspectral data enables spectrally informed learning to recover a hypercube from an RGB image. Hyperspectral learning is capable of recovering full spectroscopic resolution in the hypercube, comparable to high spectral resolutions of scientific spectrometers. Hyperspectral learning also enables ultrafast dynamic imaging, leveraging ultraslow video recording in an off-the-shelf smartphone, given that a video comprises a time series of multiple RGB images. To demonstrate its versatility, an experimental model of vascular development is used to extract hemodynamic parameters via statistical and deep-learning approaches. Subsequently, the hemodynamics of peripheral microcirculation is assessed at an ultrafast temporal resolution up to a millisecond, using a conventional smartphone camera. This spectrally informed learning method is analogous to compressed sensing; however, it further allows for reliable hypercube recovery and key feature extractions with a transparent learning algorithm. This learning-powered snapshot hyperspectral imaging method yields high spectral and temporal resolutions and eliminates the spatiospectral tradeoff, offering simple hardware requirements and potential applications of various machine-learning techniques.Comment: This paper will appear in PNAS Nexu

Nonlinear Dynamics

This volume covers a diverse collection of topics dealing with some of the fundamental concepts and applications embodied in the study of nonlinear dynamics. Each of the 15 chapters contained in this compendium generally fit into one of five topical areas: physics applications, nonlinear oscillators, electrical and mechanical systems, biological and behavioral applications or random processes. The authors of these chapters have contributed a stimulating cross section of new results, which provide a fertile spectrum of ideas that will inspire both seasoned researches and students