Some problems and errors in cytogenetic biodosimetry

Human radiosensitivity is a quantitative trait that is generally subject to binomial distribution. Individual radiosensitivity, however, may deviate significantly from the mean (by 2-3 standard deviations). Thus, the same dose of radiation may result in different levels of genotoxic damage (commonly measured as chromosome aberration rates) in different individuals. There is significant genetic component in individual radiosensitivity. It is related to carriership of variant alleles of various single-nucleotide polymorphisms (most of these in genes coding for proteins functioning in DNA damage identification and repair); carriership of different number of alleles producing cumulative effects; amplification of gene copies coding for proteins responsible for radioresistance, mobile genetic elements, and others. Among the other factors influencing individual radioresistance are: radioadaptive response; bystander effect; levels of endogenous substances with radioprotective and antimutagenic properties and environmental factors such as lifestyle and diet, physical activity, psychoemotional state, hormonal state, certain drugs, infections and others. These factors may have radioprotective or sensibilising effects. Apparently, there are too many factors that may significantly modulate the biological effects of ionising radiation. Thus, conventional methodologies for biodosimetry (specifically, cytogenetic methods) may produce significant errors if personal traits that may affect radioresistance are not accounted for.

Applications of metrological techniques for clinical implementation of dosimetry and radiobiology in molecular radiotherapy

Molecular radiotherapy (MRT) is a fast developing and promising treatment for metastasised neuroendocrine tumours. Efficacy of MRT is based on the capability to selectively "deliver" radiation to tumour cells, minimizing administered dose to normal tissues. Outcome of MRT depends on the individual patient characteristics. For that reason, personalized treatment planning is important to improve outcomes of therapy. Dosimetry plays a key role in this setting, as it is the main physical quantity related to radiation effects on cells. Dosimetry in MRT consists in a complex series of procedures ranging from imaging quantification to dose calculation. This doctoral thesis focused on several aspects concerning the clinical implementation of absorbed dose calculations in MRT. Accuracy of SPECT/CT quantification was assessed in order to determine the optimal reconstruction parameters. A model of PVE correction was developed in order to improve the activity quantification in small volume, such us lesions in clinical patterns. Advanced dosimetric methods were compared with the aim of defining the most accurate modality, applicable in clinical routine. Also, for the first time on a large number of clinical cases, the overall uncertainty of tumour dose calculation was assessed. As part of the MRTDosimetry project, protocols for calibration of SPECT/CT systems and implementation of dosimetry were drawn up in order to provide standard guidelines to the clinics offering MRT. To estimate the risk of experiencing radio-toxicity side effects and the chance of inducing damage on neoplastic cells is crucial for patient selection and treatment planning. In this thesis, the NTCP and TCP models were derived based on clinical data as help to clinicians to decide the pharmaceutical dosage in relation to the therapy control and the limitation of damage to healthy tissues. Moreover, a model for tumour response prediction based on Machine Learning analysis was developed.

Proton radiotherapy: a therapeutic opportunity for pediatric brain tumor patients

Proton radiation therapy is a form of external radiation that uses charged particles which have distinct physical advantages to deliver the majority of its dose in the target while minimizing the dose of radiation to normal tissues. In children who are particularly susceptible even at low and medium doses of radiation, the significant reduction of integral dose can potentially mitigate the incidence of side effects and improve quality of life. The aim of the first part of the thesis is to describe the physical and radiobiological properties of protons, the Proton Therapy Center of Trento (TCPT) active for clinical purpose since 2014, which use the most recent technique called active pencil beam scanning. The second part of the thesis describes the preliminary clinical results of 23 pediatric patients with central nervous system tumors as well as of two aggressive pediatric meningiomas treated with pencil beam scanning. All the patients were particularly well-suited candidates for proton therapy (PT) for possible benefits in terms of survival and incidence of acute and late side effects. We reported also a multicentric experience of 27 medulloblastoma patients (median age 6 years, M/F ratio 13/14) treated between 2015 and 2020 at TPTC coming from three Pediatric oncology centers: Bologna, Florence, and Ljubljana, with a focus on clinical results and toxicities related to radiotherapy (RT). Proton therapy was associated with mostly mild acute and late adverse effects and no cases of CNS necrosis or high grade of neuroradiological abnormailities. Comparable rates of survival and local control were obtained to those achievable with conventional RT. Finally, we performed a systematic review to specifically address the safety of PT for pediatric CNS patients, late side effects and clinical effectiveness after PT in this patient group.

Hardware-In-The Loop setup and test of an air spring control system

In this work, a Hardware-in-the-loop test bench is designed. The bench is used to test the behaviour of an electronic control unit used in Maserati to control the dynamics of an air spring system. First the mathematical model of the plant has been defined, then the simulation enviroment and the test environment have been set up. The performed tests succesfully highlighted some bugs in the device under test.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Learning from errors: Psychological, relational, and cultural aspects

Although errors might foster learning, they can also be perceived as something to avoid if they are associated with negative consequences (e.g., receiving a bad grade or being mocked by classmates). Such adverse perceptions may trigger negative emotions and error-avoidance attitudes, limiting the possibility to use errors for learning. These students’ reactions may be influenced by relational and cultural aspects of errors that characterise the learning environment. Accordingly, the main aim of this research was to investigate whether relational and cultural characteristics associated with errors affect psychological mechanisms triggered by making mistakes. In the theoretical part, we described the role of errors in learning using an integrated multilevel (i.e., psychological, relational, and cultural levels of analysis) approach. Then, we presented three studies that analysed how cultural and relational error-related variables affect psychological aspects. The studies adopted a specific empirical methodology (i.e., qualitative, experimental, and correlational) and investigated different samples (i.e., teachers, primary school pupils and middle school students). Findings of study one (cultural level) highlighted errors acquire different meanings that are associated with different teachers’ error-handling strategies (e.g., supporting or penalising errors). Study two (relational level) demonstrated that teachers’ supportive error-handling strategies promote students’ perceptions of being in a positive error climate. Findings of study three (relational and psychological level) showed that positive error climate foster students’ adaptive reactions towards errors and learning outcomes. Overall, our findings indicated that different variables influence students’ learning from errors process and teachers play an important role in conveying specific meanings of errors during learning activities, dealing with students’ mistakes supportively, and establishing an error-friendly classroom environment.

Design and characterization of automated agricultural vehicle systems

Nowadays, technological advancements have brought industry and research towards the automation of various processes. Automation brings a reduction in costs and an improvement in product quality. For this reason, companies are pushing research to investigate new technologies. The agriculture industry has always looked towards automating various processes, from product processing to storage. In the last years, the automation of harvest and cultivation phases also has become attractive, pushed by the advancement of autonomous driving. Nevertheless, ADAS systems are not enough. Merging different technologies will be the solution to obtain total automation of agriculture processes. For example, sensors that estimate products' physical and chemical properties can be used to evaluate the maturation level of fruit. Therefore, the fusion of these technologies has a key role in industrial process automation. In this dissertation, ADAS systems and sensors for precision agriculture will be both treated. Several measurement procedures for characterizing commercial 3D LiDARs will be proposed and tested to cope with the growing need for comparison tools. Axial errors and transversal errors have been investigated. Moreover, a measurement method and setup for evaluating the fog effect on 3D LiDARs will be proposed. Each presented measurement procedure has been tested. The obtained results highlight the versatility and the goodness of the proposed approaches. Regarding the precision agriculture sensors, a measurement approach for the Moisture Content and density estimation of crop directly on the field is presented. The approach regards the employment of a Near Infrared spectrometer jointly with Partial Least Square statistical analysis. The approach and the model will be described together with a first laboratory prototype used to evaluate the NIRS approach. Finally, a prototype for on the field analysis is realized and tested. The test results are promising, evidencing that the proposed approach is suitable for Moisture Content and density estimation.

Hypofractionated stereotactic radiotherapy after hyperbaric oxygenation for recurrent high-grade glioma: a pilot study

Purpose The presence of hypoxic cells in high-grade glioma (HGG) is one of the main reasons of local failure after radiotherapy (RT). The use of hyperbaric oxygen therapy (HBO) could help to overcome the problem of hypoxia in poorly oxygenated regions of the tumor. We performed a pilot study to evaluate the efficacy of hypofractionated image-guided helical TomoTherapy (HT) after HBO in the treatment of recurrent HGG (rHGG). Methods We enrolled 15 patients (aged >18 years) with diagnosis of rHGG. A total dose of 15-25 Gy was administered in daily 5-Gy fractions for 3-5 consecutive days after daily HBO. Each fraction was delivered up to maximum of 60 minutes after HBO. Results Median follow-up from HBO-RT was 28.6 (range: 5.3-56.8). No patient was lost to follow-up. Median progression-free survival (mPFS) for all patients was 3.2 months (95% CI: 1.34- 6.4 ), while 3-month, 6-month and 12 month PFS was 60% (95%CI: 31.8.4-79.7), 40% (95%CI: 16.5- 62.8) and10.0 (0.8-33.5) , respectively. Median overall survival (mOS) of HBO-RT was 11.7 months (95% CI: 7.3-29.3), while 3-month, 6-month and 12 month OS was 100% , 93.3% (61.3-99.0) and 46.7 % (21.2-68.8). No acute or late neurologic toxicity >grade 2 (CTCAE version 4.3) was observed in 86.66% of patients. Two patients developed G3 Radionecrosis. Conclusion HSRT combined to HBO seems effective and safe in the treatment of rHGG. One of advantages of HBO-RT is the reduced overall treatment time (3-5 consecutive days).

Ultra-hypofractionated stereotactic radiotherapy for prostate cancer in 5 fractions: current evidences and our clinical experience

AIMS: The present is a retrospective evaluation of acute genito-urinary (GU) and gastro-intestinal (GI) toxicity, in addition to biochemical recurrence rate in 57 prostate cancer patients treated at our Institution with ultra-hypofractionated RT (UHRT) schedule. METHODS: From January 2021 to December 2022 we have treated 57 patients with prostate cancer, using an UHRT scheme of 5-fractions every other day for a total dose delivered of 36.25 Gy, according to the PACE-B trial treatment schedule. Good urinary function, assessed by International Prostate Symptom Score (IPSS), were required. The simulation CT scans were acquired in supine position and fused with MRI for CTVs definition for every patient. Each treatment was performed by Accuray's TomoTherapy with daily IGRT. The evaluation of the set-up was very restrictive before daily treatment delivery. RESULTS: According to RTOG toxicity scale, the acute GU toxicity at 3 months from RT, GU toxicity was G0 for 30 patients (52.6%), G1 for 26 (45.6%) and G2 for one only (1.75%); rectal toxicity was G0 for 56 patients (98.25%) and G1 for one only (1.75%). The median follow-up (FU) was 9 months (2-24 months). In the following FU months, we observed progressively lower urinary and rectal toxicity, except for one patient who showed G2 GU toxicity at 12 months. All but one patient had a progressive PSA value decrease. CONCLUSIONS: In our experience, UHRT appears to be safe and well tolerated even without the use of rectal spacer devices. A longer FU is necessary to evaluate late toxicity and disease control rate.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Numerical integration errors in deep space orbit determination

Il progetto di tesi è stato sviluppato durante il periodo di tirocinio svolto all’interno del “Laboratorio di Radio Scienza ed Esplorazione Planetaria” da un'esperienza da cui prende il nome lo stesso elaborato: ”Numerical integration errors in deep space orbit determination”. Lo scopo del sopraccitato laboratorio è stato quello di studiare in modo approfondito il problema kepleriano dei due corpi, per poi passare ad un’analisi del problema dei tre corpi e successivamente a n corpi (con particolare attenzione alle orbite dei satelliti medicei di Giove). Lo studio è stato affiancato ad un costante utilizzo della piattaforma di programmazione Matlab per l’elaborazione e la stesura di codici per il calcolo di traiettorie orbitali ed errori numerici. Infatti, il fulcro del lavoro è stato proprio il confronto di vari integratori e degli errori numerici derivanti dall’integrazione. Nella tesi, dapprima, viene introdotto il sistema Gioviano, vengono presentati i satelliti medicei, delineate le caratteristiche fisiche fondamentali e i principali motivi che portano ad avere particolare interesse nel conoscere lo sviluppo orbitale di tale sistema. In seguito, l'elaborato, dopo una dettagliata descrizione teorica del problema dei due corpi, presenta un codice per la rappresentazione di orbite kepleriane e il calcolo dei relativi errori commessi dal metodo numerico rispetto a quello analitico. Nell'ultimo capitolo, invece, il problema è esteso a più corpi dotati di massa e a tal proposito viene proposto un codice per la rappresentazione delle orbite descritte nel tempo da n corpi, date le condizioni iniziali, e il calcolo dei rispettivi errori nel sistema di riferimento (r,t,n). In merito a ciò, vengono infine testati diversi integratori per cercare quello con le migliori performance e sono poi analizzati alcuni parametri in input al problema per verificare sotto quali condizioni l’integratore lavora meglio.

Cytogenetic And Morphologic Approaches Of Hybrids From Experimental Crosses Between Triatoma Lenti Sherlock & Serafim, 1967 And T. Sherlocki Papa Et Al., 2002 (hemiptera: Reduviidae).

The reproductive capacity between Triatoma lenti and Triatoma sherlocki was observed in order to verify the fertility and viability of the offspring. Cytogenetic, morphological and morphometric approaches were used to analyze the differences that were inherited. Experimental crosses were performed in both directions. The fertility rate of the eggs in crosses involving T. sherlocki females was 65% and 90% in F1 and F2 offspring, respectively. In reciprocal crosses, it was 7% and 25% in F1 and F2 offspring, respectively. The cytogenetic analyses of the male meiotic process of the hybrids were performed using lacto-acetic orcein, C-banding and Feulgen techniques. The male F1 offspring presented normal chromosome behavior, a finding that was similar to those reported in parental species. However, cytogenetic analysis of F2 offspring showed errors in chromosome pairing. This post-zygotic isolation, which prevents hybrids in nature, may represent the collapse of the hybrid. This phenomenon is due to a genetic dysregulation that occurs in the chromosomes of F1. The results were similar in the hybrids from both crosses. Morphological features, such as color and size of connexive and the presence of red-orange rings on the femora, were similar to T. sherlocki, while wins size was similar to T. lenti in F1 offspring. The eggshells showed characteristics that were similar to species of origin, whereas the median process of the pygophore resulted in intermediate characteristics in the F1 and a segregating pattern in F2 offspring. Geometric morphometric techniques used on the wings showed that both F1 and F2 offspring were similar to T. lenti. These studies on the reproductive capacity between T. lenti and T. sherlocki confirm that both species are evolutionarily closed; hence, they are included in the brasiliensis subcomplex. The extremely reduced fertility observed in the F2 hybrids confirmed the specific status of the species that were analyzed.

Ihwg-μnir: A Miniaturised Near-infrared Gas Sensor Based On Substrate-integrated Hollow Waveguides Coupled To A Micro-nir-spectrophotometer.

A miniaturised gas analyser is described and evaluated based on the use of a substrate-integrated hollow waveguide (iHWG) coupled to a microsized near-infrared spectrophotometer comprising a linear variable filter and an array of InGaAs detectors. This gas sensing system was applied to analyse surrogate samples of natural fuel gas containing methane, ethane, propane and butane, quantified by using multivariate regression models based on partial least square (PLS) algorithms and Savitzky-Golay 1(st) derivative data preprocessing. The external validation of the obtained models reveals root mean square errors of prediction of 0.37, 0.36, 0.67 and 0.37% (v/v), for methane, ethane, propane and butane, respectively. The developed sensing system provides particularly rapid response times upon composition changes of the gaseous sample (approximately 2 s) due the minute volume of the iHWG-based measurement cell. The sensing system developed in this study is fully portable with a hand-held sized analyser footprint, and thus ideally suited for field analysis. Last but not least, the obtained results corroborate the potential of NIR-iHWG analysers for monitoring the quality of natural gas and petrochemical gaseous products.

The Inadequacy Of The Y-axis Of Growth (sngn) For The Vertical Pattern Assessment In Patients With Sagittal Discrepancies.

The aim of this cephalometric study was to evaluate the influence of the sagittal skeletal pattern on the 'Y-axis of growth' measurement in patients with different malocclusions. Lateral head films from 59 patients (mean age 16y 7m, ranging from 11 to 25 years) were selected after a subjective analysis of 1630 cases. Sample was grouped as follows: Group 1 - class I facial pattern; group 2 - class II facial pattern; and Group 3 - class III facial pattern. Two angular measurements, SNGoGn and SNGn, were taken in order to determine skeletal vertical facial pattern. A logistic regression with errors distributed according to a binomial distribution was used to test the influence of the sagittal relationship (Class I, II, III facial patterns) on vertical diagnostic measurement congruence (SNGoGn and SNGn). RESULTS show that the probability of congruence between the patterns SNGn and SNGoGn was relatively high (70%) for group 1, but for groups II (46%) and III (37%) this congruence was relatively low. The use of SNGn appears to be inappropriate to determine the vertical facial skeletal pattern of patients, due to Gn point shifting throughout sagittal discrepancies. Clinical Significance: Facial pattern determined by SNGn must be considered carefully, especially when severe sagittal discrepancies are present.

Atomic Charge Transfer-counter Polarization Effects Determine Infrared Ch Intensities Of Hydrocarbons: A Quantum Theory Of Atoms In Molecules Model.

Atomic charge transfer-counter polarization effects determine most of the infrared fundamental CH intensities of simple hydrocarbons, methane, ethylene, ethane, propyne, cyclopropane and allene. The quantum theory of atoms in molecules/charge-charge flux-dipole flux model predicted the values of 30 CH intensities ranging from 0 to 123 km mol(-1) with a root mean square (rms) error of only 4.2 km mol(-1) without including a specific equilibrium atomic charge term. Sums of the contributions from terms involving charge flux and/or dipole flux averaged 20.3 km mol(-1), about ten times larger than the average charge contribution of 2.0 km mol(-1). The only notable exceptions are the CH stretching and bending intensities of acetylene and two of the propyne vibrations for hydrogens bound to sp hybridized carbon atoms. Calculations were carried out at four quantum levels, MP2/6-311++G(3d,3p), MP2/cc-pVTZ, QCISD/6-311++G(3d,3p) and QCISD/cc-pVTZ. The results calculated at the QCISD level are the most accurate among the four with root mean square errors of 4.7 and 5.0 km mol(-1) for the 6-311++G(3d,3p) and cc-pVTZ basis sets. These values are close to the estimated aggregate experimental error of the hydrocarbon intensities, 4.0 km mol(-1). The atomic charge transfer-counter polarization effect is much larger than the charge effect for the results of all four quantum levels. Charge transfer-counter polarization effects are expected to also be important in vibrations of more polar molecules for which equilibrium charge contributions can be large.