What Google Maps can do for Biomedical Data Dissemination: Examples and a Design Study

Background: Biologists often need to assess whether unfamiliar datasets warrant the time investment required for more detailed exploration. Basing such assessments on brief descriptions provided by data publishers is unwieldy for large datasets that contain insights dependent on specific scientific questions. Alternatively, using complex software systems for a preliminary analysis may be deemed as too time consuming in itself, especially for unfamiliar data types and formats. This may lead to wasted analysis time and discarding of potentially useful data. Results: We present an exploration of design opportunities that the Google Maps interface offers to biomedical data visualization. In particular, we focus on synergies between visualization techniques and Google Maps that facilitate the development of biological visualizations which have both low-overhead and sufficient expressivity to support the exploration of data at multiple scales. The methods we explore rely on displaying pre-rendered visualizations of biological data in browsers, with sparse yet powerful interactions, by using the Google Maps API. We structure our discussion around five visualizations: a gene co-regulation visualization, a heatmap viewer, a genome browser, a protein interaction network, and a planar visualization of white matter in the brain. Feedback from collaborative work with domain experts suggests that our Google Maps visualizations offer multiple, scale-dependent perspectives and can be particularly helpful for unfamiliar datasets due to their accessibility. We also find that users, particularly those less experienced with computer use, are attracted by the familiarity of the Google Maps API. Our five implementations introduce design elements that can benefit visualization developers. Conclusions: We describe a low-overhead approach that lets biologists access readily analyzed views of unfamiliar scientific datasets. We rely on pre-computed visualizations prepared by data experts, accompanied by sparse and intuitive interactions, and distributed via the familiar Google Maps framework. Our contributions are an evaluation demonstrating the validity and opportunities of this approach, a set of design guidelines benefiting those wanting to create such visualizations, and five concrete example visualizations.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

Intracranial Volume Estimation and Graph Theoretical Analysis of Brain Functional Connectivity Networks

Understanding pathways of neurological disorders requires extensive research on both functional and structural characteristics of the brain. This dissertation introduced two interrelated research endeavors, describing (1) a novel integrated approach for constructing functional connectivity networks (FCNs) of brain using non-invasive scalp EEG recordings; and (2) a decision aid for estimating intracranial volume (ICV). The approach in (1) was developed to study the alterations of networks in patients with pediatric epilepsy. Results demonstrated the existence of statistically significant (p

Acoustic and Magnetic Techniques for the Isolation and Analysis of Cells in Microfluidic Platforms

Cancer comprises a collection of diseases, all of which begin with abnormal tissue growth from various stimuli, including (but not limited to): heredity, genetic mutation, exposure to harmful substances, radiation as well as poor dieting and lack of exercise. The early detection of cancer is vital to providing life-saving, therapeutic intervention. However, current methods for detection (e.g., tissue biopsy, endoscopy and medical imaging) often suffer from low patient compliance and an elevated risk of complications in elderly patients. As such, many are looking to “liquid biopsies” for clues into presence and status of cancer due to its minimal invasiveness and ability to provide rich information about the native tumor. In such liquid biopsies, peripheral blood is drawn from patients and is screened for key biomarkers, chiefly circulating tumor cells (CTCs). Capturing, enumerating and analyzing the genetic and metabolomic characteristics of these CTCs may hold the key for guiding doctors to better understand the source of cancer at an earlier stage for more efficacious disease management.

The isolation of CTCs from whole blood, however, remains a significant challenge due to their (i) low abundance, (ii) lack of a universal surface marker and (iii) epithelial-mesenchymal transition that down-regulates common surface markers (e.g., EpCAM), reducing their likelihood of detection via positive selection assays. These factors potentiate the need for an improved cell isolation strategy that can collect CTCs via both positive and negative selection modalities as to avoid the reliance on a single marker, or set of markers, for more accurate enumeration and diagnosis.

The technologies proposed herein offer a unique set of strategies to focus, sort and template cells in three independent microfluidic modules. The first module exploits ultrasonic standing waves and a class of elastomeric particles for the rapid and discriminate sequestration of cells. This type of cell handling holds promise not only in sorting, but also in the isolation of soluble markers from biofluids. The second module contains components to focus (i.e., arrange) cells via forces from acoustic standing waves and separate cells in a high throughput fashion via free-flow magnetophoresis. The third module uses a printed array of micromagnets to capture magnetically labeled cells into well-defined compartments, enabling on-chip staining and single cell analysis. These technologies can operate in standalone formats, or can be adapted to operate with established analytical technologies, such as flow cytometry. A key advantage of these innovations is their ability to process erythrocyte-lysed blood in a rapid (and thus high throughput) fashion. They can process fluids at a variety of concentrations and flow rates, target cells with various immunophenotypes and sort cells via positive (and potentially negative) selection. These technologies are chip-based, fabricated using standard clean room equipment, towards a disposable clinical tool. With further optimization in design and performance, these technologies might aid in the early detection, and potentially treatment, of cancer and various other physical ailments.

Quantitative Analysis of Kilohertz-Frequency Neurostimulation

Mainstream electrical stimulation therapies, e.g., spinal cord stimulation (SCS) and deep brain stimulation, use pulse trains that are delivered at rates no higher than 200 Hz. In recent years, stimulation of nerve fibers using kilohertz-frequency (KHF) signals has received increased attention due to the potential to penetrate deeper in the tissue and to the ability to block conduction of action potentials. As well, there are a growing number of clinical applications that use KHF waveforms, including transcutaneous electrical stimulation (TES) for overactive bladder and SCS for chronic pain. However, there is a lack of fundamental understanding of the mechanisms of action of KHF stimulation. The goal of this research was to analyze quantitatively KHF neurostimulation.

We implemented a multilayer volume conductor model of TES including dispersion and capacitive effects, and we validated the model with in vitro measurements in a phantom constructed from dispersive materials. We quantified the effects of frequency on the distribution of potentials and fiber excitation. We also quantified the effects of a novel transdermal amplitude modulated signal (TAMS) consisting of a non-zero offset sinusoidal carrier modulated by a square-pulse train. The model revealed that high-frequency signals generated larger potentials at depth than did low frequencies, but this did not translate into lower stimulation thresholds. Both TAMS and conventional rectangular pulses activated more superficial fibers in addition to the deeper, target fibers, and at no frequency did we observe an inversion of the strength-distance relationship. In addition, we performed in vivo experiments and applied direct stimulation to the sciatic nerve of cats and rats. We measured electromyogram and compound action potential activity evoked by pulses, TAMS and modified versions of TAMS in which we varied the amplitude of the carrier. Nerve fiber activation using TAMS showed no difference with respect to activation with conventional pulse for carrier frequencies of 20 kHz and higher, regardless the size of the carrier. Therefore, TAMS with carrier frequencies >20 kHz does not offer any advantage over conventional pulses, even with larger amplitudes of the carrier, and this has implications for design of waveforms for efficient and effective TES.

We developed a double cable model of a dorsal column (DC) fiber to quantify the responses of DC fibers to a novel KHF-SCS signal. We validated the model using in vivo recordings of the strength-duration relationship and the recovery cycle of single DC fibers. We coupled the fiber model to a model of SCS in human and applied the KHF-SCS signal to quantify thresholds for activation and conduction block for different fiber diameters at different locations in the DCs. Activation and block thresholds increased sharply as the fibers were placed deeper in the DCs, and decreased for larger diameter fibers. Activation thresholds were > 5 mA in all cases and up to five times higher than for conventional (~ 50 Hz) SCS. For fibers exhibiting persistent activation, the degree of synchronization of the firing activity to the KHF-SCS signal, as quantified using the vector strength, was low for a broad amplitude range, and the dissimilarity between the activities in pairs of fibers, as quantified using the spike time distance, was high and decreased for more closely positioned fibers. Conduction block thresholds were higher than 30 mA for all fiber diameters at any depth and well above the amplitudes used clinically (0.5 – 5 mA). KHF-SCS appears to activate few, large, superficial fibers, and the activated fibers fire asynchronously to the stimulation signal and to other activated fibers.

The outcomes of this work contribute to the understanding of KHF neurostimulation by establishing the importance of the tissue filtering properties on the distribution of potentials, assessing quantitatively the impact of KHF stimulation on nerve fiber excitation, and developing and validating a detailed model of a DC fiber to characterize the effects of KHF stimulation on DC axons. The results have implications for design of waveforms for efficient and effective nerve fiber stimulation in the peripheral and central nervous system.

Statistical modelling of the rheological and mucoadhesive properties of aqueous poly(methylvinylether-co-maleic acid) networks: Redefining biomedical applications and the relationship between viscoelasticity and mucoadhesion

Poly(methylvinylether-co-maleic acid) (PMVE/MA) is commonly used as a component of pharmaceutical platforms, principally to enhance interactions with biological substrates (mucoadhesion). However, the limited knowledge on the rheological properties of this polymer and their relationships with mucoadhesion has negated the biomedical use of this polymer as a mono-component platform. This study presents a comprehensive study of the rheological properties of aqueous PMVE/MA platforms and defines their relationships with mucoadhesion using multiple regression analysis. Using dilute solution viscometry the intrinsic viscosities of un-neutralised PMVE/MA and PMVE/MA neutralised using NaOH or TEA were 22.32 ± 0.89 dL g-1, 274.80 ± 1.94 dL g-1 and 416.49 ± 2.21 dL g-1 illustrating greater polymer chain expansion following neutralisation using Triethylamine (TEA). PMVE/MA platforms exhibited shear-thinning properties. Increasing polymer concentration increased the consistencies, zero shear rate (ZSR) viscosities (determined from flow rheometry), storage and loss moduli, dynamic viscosities (defined using oscillatory analysis) and mucoadhesive properties, yet decreased the loss tangents of the neutralised polymer platforms. TEA neutralised systems possessed significantly and substantially greater consistencies, ZSR and dynamic viscosities, storage and loss moduli, mucoadhesion and lower loss tangents than their NaOH counterparts. Multiple regression analysis enabled identification of the dominant role of polymer viscoelasticity on mucoadhesion (r > 0.98). The mucoadhesive properties of PMVE/MA platforms were considerable and were greater than those of other platforms that have successfully been shown to enhance in vivo retention when applied to the oral cavity, indicating a positive role for PMVE/MA mono-component platforms for pharmaceutical and biomedical applications.

Hybrid materials for biomedical applications

The increased longevity of humans and the demand for a better quality of life have led to a continuous search for new implant materials. Scientific development coupled with a growing multidisciplinarity between materials science and life sciences has given rise to new approaches such as regenerative medicine and tissue engineering. The search for a material with mechanical properties close to those of human bone produced a new family of hybrid materials that take advantage of the synergy between inorganic silica (SiO4) domains, based on sol-gel bioactive glass compositions, and organic polydimethylsiloxane, PDMS ((CH3)2.SiO2)n, domains. Several studies have shown that hybrid materials based on the system PDMS-SiO2 constitute a promising group of biomaterials with several potential applications from bone tissue regeneration to brain tissue recovery, passing by bioactive coatings and drug delivery systems. The objective of the present work was to prepare hybrid materials for biomedical applications based on the PDMS-SiO2 system and to achieve a better understanding of the relationship among the sol-gel processing conditions, the chemical structures, the microstructure and the macroscopic properties. For that, different characterization techniques were used: Fourier transform infrared spectrometry, liquid and solid state nuclear magnetic resonance techniques, X-ray diffraction, small-angle X-ray scattering, smallangle neutron scattering, surface area analysis by Brunauer–Emmett–Teller method, scanning electron microscopy and transmission electron microscopy. Surface roughness and wettability were analyzed by 3D optical profilometry and by contact angle measurements respectively. Bioactivity was evaluated in vitro by immersion of the materials in Kokubos’s simulated body fluid and posterior surface analysis by different techniques as well as supernatant liquid analysis by inductively coupled plasma spectroscopy. Biocompatibility was assessed using MG63 osteoblastic cells. PDMS-SiO2-CaO materials were first prepared using nitrate as a calcium source. To avoid the presence of nitrate residues in the final product due to its potential toxicity, a heat-treatment step (above 400 °C) is required. In order to enhance the thermal stability of the materials subjected to high temperatures titanium was added to the hybrid system, and a material containing calcium, with no traces of nitrate and the preservation of a significant amount of methyl groups was successfully obtained. The difficulty in eliminating all nitrates from bulk PDMS-SiO2-CaO samples obtained by sol-gel synthesis and subsequent heat-treatment created a new goal which was the search for alternative sources of calcium. New calcium sources were evaluated in order to substitute the nitrate and calcium acetate was chosen due to its good solubility in water. Preparation solgel protocols were tested and homogeneous monolithic samples were obtained. Besides their ability to improve the bioactivity, titanium and zirconium influence the structural and microstructural features of the SiO2-TiO2 and SiO2-ZrO2 binary systems, and also of the PDMS-TiO2 and PDMS-ZrO2 systems. Detailed studies with different sol-gel conditions allowed the understanding of the roles of titanium and zirconium as additives in the PDMS-SiO2 system. It was concluded that titanium and zirconium influence the kinetics of the sol-gel process due to their different alkoxide reactivity leading to hybrid xerogels with dissimilar characteristics and morphologies. Titanium isopropoxide, less reactive than zirconium propoxide, was chosen as source of titanium, used as an additive to the system PDMS-SiO2-CaO. Two different sol-gel preparation routes were followed, using the same base composition and calcium acetate as calcium source. Different microstructures with high hydrophobicit were obtained and both proved to be biocompatible after tested with MG63 osteoblastic cells. Finally, the role of strontium (typically known in bioglasses to promote bone formation and reduce bone resorption) was studied in the PDMS-SiO2-CaOTiO2 hybrid system. A biocompatible material, tested with MG63 osteoblastic cells, was obtained with the ability to release strontium within the values reported as suitable for bone tissue regeneration.

A study on independent component analysis over galois fields

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Point-of-care lactate and creatinine analysis for sick obstetric patients at Queen Elizabeth Central Hospital in Blantyre, Malawi: A feasibility study

Background: To achieve good outcomes in critically ill obstetric patients, it is necessary to identify organ dysfunction rapidly so that life-saving interventions can be appropriately commenced. However, timely access to clinical chemistry results is problematic, even in referral institutions, in the sub-Saharan African region. Reliable point-of-care tests licensed for clinical use are now available for lactate and creatinine. Aim: We aimed to assess whether implementation of point-of-care testing for lactate and creatinine is feasible in the obstetric unit at the Queen Elizabeth Central Hospital (QECH) in Blantyre, Malawi, by obtaining the opinions of clinical staff on the use of these tests in practice. Methods: During a two-month evaluation period nurse-midwives, medical interns, clinical officers, registrars, and consultants were given the opportunity to use StatStrip® and StatSensor® (Nova Biomedical, Waltham, USA) devices, for lactate and creatinine estimation, as part of their routine clinical practice in the obstetric unit. They were subsequently asked to complete a short questionnaire. Results: Thirty-seven questionnaires were returned by participants: 22 from nurse-midwives and the remainder from clinicians. The mean satisfaction score for the devices was 7.6/10 amongst clinicians and 8.0/10 amongst nurse-midwives. The majority of participants stated that the obstetric high dependency unit (HDU) was the most suitable location for the devices. For lactate, 31 participants strongly agreed that testing should be continued and 24 strongly agreed that it would influence patient management. For creatinine, 29 strongly agreed that testing should be continued and 28 strongly agreed that it would influence their patient management. Twenty participants strongly agreed that they trust point-of-care devices. Conclusions: Point-of-care clinical chemistry testing was feasible, practical, and well received by staff, and was considered to have a useful role to play in the clinical care of sick obstetric patients at this referral centre.

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.

Non-Equilibrium Atmospheric Plasma As A Novel Route To Nanomaterial Synthesis And Processing For Biomedical Applications

Since last century, the rising interest of value-added and advanced functional materials has spurred a ceaseless development in terms of industrial processes and applications. Among the emerging technologies, thanks to their unique features and versatility in terms of supported processes, non-equilibrium plasma discharges appear as a key solvent-free, high-throughput and cost-efficient technique. Nevertheless, applied research studies are needed with the aim of addressing plasma potentialities optimizing devices and processes for future industrial applications. In this framework, the aim of this dissertation is to report on the activities carried out and the results achieved concerning the development and optimization of plasma techniques for nanomaterial synthesis and processing to be applied in the biomedical field. In the first section, the design and investigation of a plasma assisted process for the production of silver (Ag) nanostructured multilayer coatings exhibiting anti-biofilm and anti-clot properties is described. With the aim on enabling in-situ and on-demand deposition of Ag nanoparticles (NPs), the optimization of a continuous in-flight aerosol process for particle synthesis is reported. The stability and promising biological performances of deposited coatings spurred further investigation through in-vitro and in-vivo tests which results are reported and discussed. With the aim of addressing the unanswered questions and tuning NPs functionalities, the second section concerns the study of silver containing droplet conversion in a flow-through plasma reactor. The presented results, obtained combining different analysis techniques, support a formation mechanism based on droplet to particle conversion driven by plasma induced precursor reduction. Finally, the third section deals with the development of a simulative and experimental approach used to investigate the in-situ droplet evaporation inside the plasma discharge addressing the main contributions to liquid evaporation in the perspective of process industrial scale up.

Edge-aware Graph Attention Networks: Joint Reasoning On Text and Knowledge Graphs for Biomedical Question Answering

Much of the real-world dataset, including textual data, can be represented using graph structures. The use of graphs to represent textual data has many advantages, mainly related to maintaining a more significant amount of information, such as the relationships between words and their types. In recent years, many neural network architectures have been proposed to deal with tasks on graphs. Many of them consider only node features, ignoring or not giving the proper relevance to relationships between them. However, in many node classification tasks, they play a fundamental role. This thesis aims to analyze the main GNNs, evaluate their advantages and disadvantages, propose an innovative solution considered as an extension of GAT, and apply them to a case study in the biomedical field. We propose the reference GNNs, implemented with methodologies later analyzed, and then applied to a question answering system in the biomedical field as a replacement for the pre-existing GNN. We attempt to obtain better results by using models that can accept as input both node and edge features. As shown later, our proposed models can beat the original solution and define the state-of-the-art for the task under analysis.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Analysis Of The Dna Fourier Transform-infrared Microspectroscopic Signature Using An All-reflecting Objective.

The Fourier transform-infrared (FT-IR) signature of dry samples of DNA and DNA-polypeptide complexes, as studied by IR microspectroscopy using a diamond attenuated total reflection (ATR) objective, has revealed important discriminatory characteristics relative to the PO2(-) vibrational stretchings. However, DNA IR marks that provide information on the sample's richness in hydrogen bonds have not been resolved in the spectral profiles obtained with this objective. Here we investigated the performance of an all reflecting objective (ARO) for analysis of the FT-IR signal of hydrogen bonds in DNA samples differing in base richness types (salmon testis vs calf thymus). The results obtained using the ARO indicate prominent band peaks at the spectral region representative of the vibration of nitrogenous base hydrogen bonds and of NH and NH2 groups. The band areas at this spectral region differ in agreement with the DNA base richness type when using the ARO. A peak assigned to adenine was more evident in the AT-rich salmon DNA using either the ARO or the ATR objective. It is concluded that, for the discrimination of DNA IR hydrogen bond vibrations associated with varying base type proportions, the use of an ARO is recommended.