Waiting Time Screening in Diagnostic Medical Imaging - A Case-Based View

Due to the high standards expected from diagnostic medical imaging, the analysis of information regarding waiting lists via different information systems is of utmost importance. Such analysis, on the one hand, may improve the diagnostic quality and, on the other hand, may lead to the reduction of waiting times, with the concomitant increase of the quality of services and the reduction of the inherent financial costs. Hence, the purpose of this study is to assess the waiting time in the delivery of diagnostic medical imaging services, like computed tomography and magnetic resonance imaging. Thereby, this work is focused on the development of a decision support system to assess waiting times in diagnostic medical imaging with recourse to operational data of selected attributes extracted from distinct information systems. The computational framework is built on top of a Logic Programming Case-base Reasoning approach to Knowledge Representation and Reasoning that caters for the handling of in-complete, unknown, or even self-contradictory information.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

On Reasonable Space and Time Cost Models for the λ-Calculus

Slot and van Emde Boas Invariance Thesis states that a time (respectively, space) cost model is reasonable for a computational model C if there are mutual simulations between Turing machines and C such that the overhead is polynomial in time (respectively, linear in space). The rationale is that under the Invariance Thesis, complexity classes such as LOGSPACE, P, PSPACE, become robust, i.e. machine independent. In this dissertation, we want to find out if it possible to define a reasonable space cost model for the lambda-calculus, the paradigmatic model for functional programming languages. We start by considering an unusual evaluation mechanism for the lambda-calculus, based on Girard's Geometry of Interaction, that was conjectured to be the key ingredient to obtain a space reasonable cost model. By a fine complexity analysis of this schema, based on new variants of non-idempotent intersection types, we disprove this conjecture. Then, we change the target of our analysis. We consider a variant over Krivine's abstract machine, a standard evaluation mechanism for the call-by-name lambda-calculus, optimized for space complexity, and implemented without any pointer. A fine analysis of the execution of (a refined version of) the encoding of Turing machines into the lambda-calculus allows us to conclude that the space consumed by this machine is indeed a reasonable space cost model. In particular, for the first time we are able to measure also sub-linear space complexities. Moreover, we transfer this result to the call-by-value case. Finally, we provide also an intersection type system that characterizes compositionally this new reasonable space measure. This is done through a minimal, yet non trivial, modification of the original de Carvalho type system.

Orchestration of multiscale model for computational oncology

Cancer is a challenging disease that involves multiple types of biological interactions in different time and space scales. Often computational modelling has been facing problems that, in the current technology level, is impracticable to represent in a single space-time continuum. To handle this sort of problems, complex orchestrations of multiscale models is frequently done. PRIMAGE is a large EU project that aims to support personalized childhood cancer diagnosis and prognosis. The goal is to do so predicting the growth of the solid tumour using multiscale in-silico technologies. The project proposes an open cloud-based platform to support decision making in the clinical management of paediatric cancers. The orchestration of predictive models is in general complex and would require a software framework that support and facilitate such task. The present work, proposes the development of an updated framework, referred herein as the VPH-HFv3, as a part of the PRIMAGE project. This framework, a complete re-writing with respect to the previous versions, aims to orchestrate several models, which are in concurrent development, using an architecture as simple as possible, easy to maintain and with high reusability. This sort of problem generally requires unfeasible execution times. To overcome this problem was developed a strategy of particularisation, which maps the upper-scale model results into a smaller number and homogenisation which does the inverse way and analysed the accuracy of this approach.

Deep learning and embeddings for problems of computational biology

The development of Next Generation Sequencing promotes Biology in the Big Data era. The ever-increasing gap between proteins with known sequences and those with a complete functional annotation requires computational methods for automatic structure and functional annotation. My research has been focusing on proteins and led so far to the development of three novel tools, DeepREx, E-SNPs&GO and ISPRED-SEQ, based on Machine and Deep Learning approaches. DeepREx computes the solvent exposure of residues in a protein chain. This problem is relevant for the definition of structural constraints regarding the possible folding of the protein. DeepREx exploits Long Short-Term Memory layers to capture residue-level interactions between positions distant in the sequence, achieving state-of-the-art performances. With DeepRex, I conducted a large-scale analysis investigating the relationship between solvent exposure of a residue and its probability to be pathogenic upon mutation. E-SNPs&GO predicts the pathogenicity of a Single Residue Variation. Variations occurring on a protein sequence can have different effects, possibly leading to the onset of diseases. E-SNPs&GO exploits protein embeddings generated by two novel Protein Language Models (PLMs), as well as a new way of representing functional information coming from the Gene Ontology. The method achieves state-of-the-art performances and is extremely time-efficient when compared to traditional approaches. ISPRED-SEQ predicts the presence of Protein-Protein Interaction sites in a protein sequence. Knowing how a protein interacts with other molecules is crucial for accurate functional characterization. ISPRED-SEQ exploits a convolutional layer to parse local context after embedding the protein sequence with two novel PLMs, greatly surpassing the current state-of-the-art. All methods are published in international journals and are available as user-friendly web servers. They have been developed keeping in mind standard guidelines for FAIRness (FAIR: Findable, Accessible, Interoperable, Reusable) and are integrated into the public collection of tools provided by ELIXIR, the European infrastructure for Bioinformatics.

Computational Analysis Based on Parametric Models of Historic Masonry Vaulted Structures Strengthened by Fiber-Reinforced Composite Materials

Historic vaulted masonry structures often need strengthening interventions that can effectively improve their structural performance, especially during seismic events, and at the same time respect the existing setting and the modern conservation requirements. In this context, the use of innovative materials such as fiber-reinforced composite materials has been shown as an effective solution that can satisfy both aspects. This work aims to provide insight into the computational modeling of a full-scale masonry vault strengthened by fiber-reinforced composite materials and analyze the influence of the arrangement of the reinforcement on the efficiency of the intervention. At first, a parametric model of a cross vault focusing on a realistic representation of its micro-geometry is proposed. Then numerical modeling, simulating the pushover analyses, of several barrel vaults reinforced with different reinforcement configurations is performed. Finally, the results are collected and discussed in terms of force-displacement curves obtained for each proposed configuration.

Identification Of Corynebacterium Spp. Isolated From Bovine Intramammary Infections By Matrix-assisted Laser Desorption Ionization-time Of Flight Mass Spectrometry.

Corynebacterium species (spp.) are among the most frequently isolated pathogens associated with subclinical mastitis in dairy cows. However, simple, fast, and reliable methods for the identification of species of the genus Corynebacterium are not currently available. This study aimed to evaluate the usefulness of matrix-assisted laser desorption ionization/mass spectrometry (MALDI-TOF MS) for identifying Corynebacterium spp. isolated from the mammary glands of dairy cows. Corynebacterium spp. were isolated from milk samples via microbiological culture (n=180) and were analyzed by MALDI-TOF MS and 16S rRNA gene sequencing. Using MALDI-TOF MS methodology, 161 Corynebacterium spp. isolates (89.4%) were correctly identified at the species level, whereas 12 isolates (6.7%) were identified at the genus level. Most isolates that were identified at the species level with 16 S rRNA gene sequencing were identified as Corynebacterium bovis (n=156; 86.7%) were also identified as C. bovis with MALDI-TOF MS. Five Corynebacterium spp. isolates (2.8%) were not correctly identified at the species level with MALDI-TOF MS and 2 isolates (1.1%) were considered unidentified because despite having MALDI-TOF MS scores >2, only the genus level was correctly identified. Therefore, MALDI-TOF MS could serve as an alternative method for species-level diagnoses of bovine intramammary infections caused by Corynebacterium spp.

Interplay Between Cell Growth And Cell Cycle In Plants.

The growth of organs and whole plants depends on both cell growth and cell-cycle progression, but the interaction between both processes is poorly understood. In plants, the balance between growth and cell-cycle progression requires coordinated regulation of four different processes: macromolecular synthesis (cytoplasmic growth), turgor-driven cell-wall extension, mitotic cycle, and endocycle. Potential feedbacks between these processes include a cell-size checkpoint operating before DNA synthesis and a link between DNA contents and maximum cell size. In addition, key intercellular signals and growth regulatory genes appear to target at the same time cell-cycle and cell-growth functions. For example, auxin, gibberellin, and brassinosteroid all have parallel links to cell-cycle progression (through S-phase Cyclin D-CDK and the anaphase-promoting complex) and cell-wall functions (through cell-wall extensibility or microtubule dynamics). Another intercellular signal mediated by microtubule dynamics is the mechanical stress caused by growth of interconnected cells. Superimposed on developmental controls, sugar signalling through the TOR pathway has recently emerged as a central control point linking cytoplasmic growth, cell-cycle and cell-wall functions. Recent progress in quantitative imaging and computational modelling will facilitate analysis of the multiple interconnections between plant cell growth and cell cycle and ultimately will be required for the predictive manipulation of plant growth.

A Metabolomic Protocol For Plant Systematics By Matrix-assisted Laser-desorption/ionization Time-of Flight Mass Spectrometry.

Matrix-assisted laser desorption/ionization time-of flight mass spectrometry (MALDI-TOF MS) has been widely used for the identification and classification of microorganisms based on their proteomic fingerprints. However, the use of MALDI-TOF MS in plant research has been very limited. In the present study, a first protocol is proposed for metabolic fingerprinting by MALDI-TOF MS using three different MALDI matrices with subsequent multivariate data analysis by in-house algorithms implemented in the R environment for the taxonomic classification of plants from different genera, families and orders. By merging the data acquired with different matrices, different ionization modes and using careful algorithms and parameter selection, we demonstrate that a close taxonomic classification can be achieved based on plant metabolic fingerprints, with 92% similarity to the taxonomic classifications found in literature. The present work therefore highlights the great potential of applying MALDI-TOF MS for the taxonomic classification of plants and, furthermore, provides a preliminary foundation for future research.

Profile Of Phenolic Compounds Of Brazilian Virgin Olive Oils By Rapid Resolution Liquid Chromatography Coupled To Electrospray Ionisation Time-of-flight Mass Spectrometry (rrlc-esi-tof-ms).

In recent years, agronomical researchers began to cultivate several olive varieties in different regions of Brazil to produce virgin olive oil (VOO). Because there has been no reported data regarding the phenolic profile of the first Brazilian VOO, the aim of this work was to determine phenolic contents of these samples using rapid-resolution liquid chromatography coupled to electrospray ionisation time-of-flight mass spectrometry. 25 VOO samples from Arbequina, Koroneiki, Arbosana, Grappolo, Manzanilla, Coratina, Frantoio and MGS Mariense varieties from three different Brazilian states and two crops were analysed. It was possible to quantify 19 phenolic compounds belonging to different classes. The results indicated that Brazilian VOOs have high total phenolic content because the values were comparable with those from high-quality VOOs produced in other countries. VOOs from Coratina, Arbosana and Grappolo presented the highest total phenolic content. These data will be useful in the development and improvement of Brazilian VOO.

Effect of cement type and water storage time on the push-out bond strength of a glass fiber post

This study investigated the effects of the cement type and the water storage time on the push-out bond strength of a glass fiber post. Glass fiber posts (Fibrekor, Jeneric Pentron) were luted to post spaces using a self-cured resin cement (C&B Cement [CB]), a glass ionomer cement (Ketac Cem [KC]) or a resin-modified glass ionomer cement (GC FujiCEM [FC]) according to the manufacturers’ instructions. For each luting agent, the specimens were exposed to one of the following water storage times (n=5): 1 day (T1), 7 days (T7), 90 days (T90) and 180 days (T180). Push-out tests were performed after the storage times. Control specimens were not exposed to water storage, but subjected to the push-out test 10 min after post cementation. Data (in MPa) were analyzed by Kruskal-Wallis and Dunn`s test (α=0.05). Cement type and water storage time had a significant effect (p<0.05) on the push-out bond strength. CB showed significantly higher values of retention (p<0.05) than KC and FC, irrespective of the water storage time. Water storage increased significantly the push-out bond strength in T7 and T90, regardless of the cement type (p<0.05). The results showed that fiber posts luted to post spaces with the self-cured resin cement exhibited the best bonding performance throughout the 180-day water storage period. All cements exhibited a tendency to increase the bond strength after 7 and 90 days of water storage, decreasing thereafter.

Effects of light-curing time on the cytotoxicity of a restorative composite resin on odontoblast-like cells

This in vitro study evaluated the cytotoxicity of an experimental restorative composite resin subjected to different light-curing regimens. METHODS: Forty round-shaped specimens were prepared and randomly assigned to four experimental groups (n=10), as follows: in Group 1, no light-curing; in Groups 2, 3 and 4, the composite resin specimens were light-cured for 20, 40 or 60 s, respectively. In Group 5, filter paper discs soaked in 5 µL PBS were used as negative controls. The resin specimens and paper discs were placed in wells of 24-well plates in which the odontoblast-like cells MDPC-23 (30,000 cells/cm²) were plated and incubated in a humidified incubator with 5% CO2 and 95% air at 37ºC for 72 h. The cytotoxicity was evaluated by the cell metabolism (MTT assay) and cell morphology (SEM). The data were analyzed statistically by Kruskal-Wallis and Mann-Whitney tests (p<0.05). RESULTS: In G1, cell metabolism decreased by 86.2%, indicating a severe cytotoxicity of the non-light-cured composite resin. On the other hand, cell metabolism decreased by only 13.3% and 13.5% in G2 and G3, respectively. No cytotoxic effects were observed in G4 and G5. In G1, only a few round-shaped cells with short processes on their cytoplasmic membrane were observed. In the other experimental groups as well as in control group, a number of spindle-shaped cells with long cytoplasmic processes were found. CONCLUSION: Regardless of the photoactivation time used in the present investigation, the experimental composite resin presented mild to no toxic effects to the odontoblast-like MDPC-23 cells. However, intense cytotoxic effects occurred when no light-curing was performed.

Surface and subsurface erosion of primary enamel by acid beverages over time

This study evaluated the influence of a cola-type soft drink and a soy-based orange juice on the surface and subsurface erosion of primary enamel, as a function of the exposure time. Seventy-five primary incisors were divided for microhardness test (n=45) or scanning electron microscopy (SEM) analysis (n=30). The specimens were randomly assigned to 3 groups: 1 - artificial saliva (control); 2 - cola-type soft drink; and 3 - soy-based orange juice. Immersion cycles in the beverages were undertaken under agitation for 5 min, 3 times a day, during 60 days. Surface microhardness was measured at 7, 15, 30, 45 and 60 days. After 60 days, specimens were bisected and subsurface microhardness was measured at 30, 60, 90, 120, 150 and 200 µm from the surface exposed. Data were analyzed by ANOVA and Tukey’s test (a=0.05). Groups 2 and 3 presented similar decrease of surface microhardness. Regarding subsurface microhardness, group 2 presented the lowest values. SEM images revealed that after 60 days the surfaces clearly exhibited structural loss, unlike those immersed in artificial saliva. It may be concluded that erosion of the surfaces exposed to the cola-type soft drink was more accentuated and directly proportional to the exposure time.

Influence of surface sealant on the translucency of composite resin: effect of immersion time and immersion media

This study evaluated the effect of surface sealant on the translucency of composite resin immersed in different solutions. The study involved the following materials: Charisma, Fortify and coffee, Coca-Cola®, tea and artificial saliva as solutions. Sixty-four specimens (n = 8) were manufactured and immersed in artificial saliva at 37 ± 1 °C. Samples were immersed in the solutions for three times a day and re-immersed in artificial saliva until the translucency readings. The measurements were carried out at nine times: T1 - 24 hours after specimen preparation, T2 - 24 hours after immersion in the solutions, T3 - 48 hours and T4 to T9 - 7, 14, 21, 30, 60 and 90 days, respectively, after immersion. The translucency values were measured using a JOUAN device. The results were subjected to ANOVA and Tukey's test at 5%. The surface sealant was not able to protect the composite resin against staining, the coffee showed the strongest staining action, followed by tea and regarding immersion time, a significant alteration was noted in the translucency of composite resin after 21 days.

In vitro analysis of thermocompaction time and gutta-percha type on quality of main canal and lateral canals filling

The aim of this study was to evaluate the quality of filling in main and lateral root canals performed with the McSpadden technique, regarding the time spent on the procedure and the type of gutta-percha employed. Fifty simulated root canals, made with six lateral canals placed two apiece in the cervical, middle and apical thirds of the root, were divided into 5 groups. Group A: McSpadden technique with conventional gutta-percha, performed with sufficient time for canal filling; Group B: McSpadden technique with conventional gutta-percha, performed in twice the mean time used in Group A; Group C: McSpadden technique with TP gutta-percha, performed with sufficient time for canal filling; Group D: McSpadden technique with TP gutta-percha, performed in twice the mean time used in Group C; Group E: lateral condensation technique. Images of the filled root canals were taken using a stereomicroscope and analyzed using the Leica QWIN Pro software for filling material flow, gutta-percha filling extension and sealer flow. Data were analyzed by analysis of variance (ANOVA) and Tukey test (p < 0.05). The best values of penetration in lateral canals in the middle third occurred in the groups where TP gutta-percha was used. However, in the apical third, group B showed the best values. Although a longer time of compactor use allows greater penetration of the filling material into the lateral canals, the presence of voids resulted in bad quality radiographic images, suggesting porosity. The best quality of filling material was observed in Group A (McSpadden technique with conventional Gutta-Percha, performed with sufficient time for root canal filling).