One dimensional photonic crystal for label-free and fluorescence sensing application

The development of more sensitive and more reliable sensors aids medical applications in many fields as diseases detection or therapy progress. This thesis threats the development of an optical biosensor based on electromagnetic modes propagating at the interface between a finite one-dimensional photonic crystal (1DPC) and a homogeneous external medium, also named Bloch Surface Waves (BSW). BSW have emerged as an attractive approach for label-free sensing in plasmon-like sensor configurations. Besides label-free operation, the large field enhancement and the absence of quenching allow the use of BSW to excite fluorescent labels that are in proximity of the 1DPC surface. This approach was adapted to the case of angularly resolved resonance detection, thus giving rise to a combined label-free/labelled biosensor platform. BSW present many degrees of design freedomthat enable tuning of resonance properties. In order to obtain a figure of merit for an optimization, I investigated the measurement uncertainty depending on resonance width and depth with different numericalmodels. This has led to a limit of detection that can assist the choice of the best design to use. Two tumor biomarkers, such as vascular endothelial growth factor (VEGF) and Angiopoietin-2 (Ang2), have been considered to be detected with the BSW biosensing platform. For this purpose the specific antibodies for the two tumor biomarkers were immobilized on the 1DPC biochip surface. The conclusive experiments reported in this work demonstrated the successful detection of the VEGF biomarker in complex matrices, such as cell culture supernatants and human plasma samples. Moreover, the platformwas used to determinate Ang2 concentration in untreated human plasma samples using low volumes, 300 μL, and with short turnaround times, 30 minutes. This is the first BSW based biosensor assay for the determination of tumor biomarker in human plasma samples at clinically relevant concentrations

Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions

Design rules for combined label-free and fluorescence Bloch surface wave biosensors

We report on the fabrication and physical characterization of optical biosensors implementing simultaneous label-free and fluorescence detection and taking advantage of the excitation of Bloch surface waves at a photonic crystal’s truncation interface. Two types of purposely-designed one dimensional photonic crystals on molded organic substrates with micro-optics were fabricated. These feature either high or low finesse of the Bloch surface wave resonances and were tested on the same optical readout system. The experimental results show that designing biochips with a large resonance quality factor does not necessarily lead in the real case to an improvement of the biosensor performance. Conditions for optimal biochips’ design and operation of the complete bio-sensing platform are established

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions

Glass transition and cooperative rearranging regions in amorphous thermoplastic nanocomposites

The aim of this work was to study the effect of nanofillers on the structural relaxation phenomena occurring in amorphous poly(ethylene-terephthalate)/poly(cyclohexane-dimethanol terephthalate) copolymer (PET/PCHDMT) nanocomposites in correspondence with the glass transition temperature. PET/PCHDMT nanocomposites were prepared by melt mixing with an organicmodified montmorillonite at different processing temperatures. Differential scanning calorimetry analysis revealed that addition of the organic modifier alone causes a decrease of the glass transition temperature and an increase of the specific heat discontinuity. Nanocomposites showed a higher glass transition temperature and a lower specific heat discontinuity compared with samples obtained by adding organic modifier to PET/PCHDMT. Both effects were more relevant for samples processed at lower temperatures. Therefore, the glass transition temperature was studied by introducing the concept of fictive temperature and relaxation time. It was found that nanocomposites have a higher apparent activation energy and an increased size of cooperatively rearranging regions compared with neat PET/PCHDMT. Both effects are more relevant for nanocomposites processed at lower temperatures. All the discussed effects are explained by considering the enhanced confinement of PET/PCHDMT macromolecules, due to the presence of intercalated lamellae of organofiller. The efficiency of intercalation is increased by decreased processing temperature, which involves an increase of the nano-confinement area of the polymer

Ataxia in children: early recognition and clinical evaluation

Background: Ataxia is a sign of different disorders involving any level of the nervous system and consisting of impaired coordination of movement and balance. It is mainly caused by dysfunction of the complex circuitry connecting the basal ganglia, cerebellum and cerebral cortex. A careful history, physical examination and some characteristic maneuvers are useful for the diagnosis of ataxia. Some of the causes of ataxia point toward a benign course, but some cases of ataxia can be severe and particularly frightening. Methods: Here, we describe the primary clinical ways of detecting ataxia, a sign not easily recognizable in children. We also report on the main disorders that cause ataxia in children. Results: The causal events are distinguished and reported according to the course of the disorder: acute, intermittent, chronic-non-progressive and chronic-progressive. Conclusions: Molecular research in the field of ataxia in children is rapidly expanding; on the contrary no similar results have been attained in the field of the treatment since most of the congenital forms remain fully untreatable. Rapid recognition and clinical evaluation of ataxia in children remains of great relevance for therapeutic results and prognostic counseling

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

Background: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. Results: In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel levels are devoted to catching both non-periodic and periodic DNA string features. A dense layer is devoted to their combination to give a final classification. Conclusions: Results computed on public data sets of different organisms show that CORENup is a state of the art methodology for nucleosome positioning identification based on a Deep Neural Network architecture. The comparisons have been carried out using two groups of datasets, currently adopted by the best performing methods, and CORENup has shown top performance both in terms of classification metrics and elapsed computation time

Humanoid Introspection: A Practical Approach

Abstract We describe an approach to robot introspection based on self observation and communication. Self observation is what the robot should do in order to build, represent and understand its internal state. It is necessary to translate the state representation in order to build a suitable input to an ontology that supplies the meaning of the internal state. The ontology supports the linguistic level that is used to communicate information about the robot state to the human user

Fuzzy Clustering of Histopathological Images Using Deep Learning Embeddings

Metric learning is a machine learning approach that aims to learn a new distance metric by increasing (reducing) the similarity of examples belonging to the same (different) classes. The output of these approaches are embeddings, where the input data are mapped to improve a crisp or fuzzy classification process. The deep metric learning approaches regard metric learning, implemented by using deep neural networks. Such models have the advantage to discover very representative nonlinear embeddings. In this work, we propose a triplet network deep metric learning approach, based on ResNet50, to find a representative embedding for the unsupervised fuzzy classification of benign and malignant histopathological images of breast cancer tissues. Experiments computed on the BreakHis benchmark dataset, using Fuzzy C-Means Clustering, show the benefit of using very low dimensional embeddings found by the deep metric learning approach