Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Combined Evaluation of ST-segment Elevation in lead AVR and ST-segment Depression in other Leads Enhances Prediction of in-hospital Cardiovascular Death in Patients with Non ST-segment Elevation Acute Coronary Syndrome

Objective: To investigate the prognostic significance of ST-segment elevation (STE) in aVR associated with ST-segment depression (STD) in other leads in patients with non-STE acute coronary syndrome (NSTE-ACS). Background: In NSTE-ACS patients, STD has been extensively associated with severe coronary lesions and poor outcomes. The prognostic role of STE in aVR is uncertain. Methods: We enrolled 888 consecutive patients with NSTE-ACS. They were divided into two groups according to the presence or not on admission ECG of aVR STE≥ 1mm and STD (defined as high risk ECG pattern). The primary and secondary endpoints were: in-hospital cardiovascular (CV) death and the rate of culprit left main disease (LMD). Results: Patients with high risk ECG pattern (n=121) disclosed a worse clinical profile compared to patients (n=575) without [median GRACE (Global-Registry-of-Acute-Coronary-Events) risk score =142 vs. 182, respectively]. A total of 75% of patients underwent coronary angiography. The rate of in-hospital CV death was 3.9%. On multivariable analysis patients who had the high risk ECG pattern showed an increased risk of CV death (OR=2.88, 95%CI 1.05-7.88) and culprit LMD (OR=4.67,95%CI 1.86-11.74) compared to patients who had not. The prognostic significance of the high risk ECG pattern was maintained even after adjustment for the GRACE risk score (OR = 2.28, 95%CI:1.06-4.93 and OR = 4.13, 95%CI:2.13-8.01, for primary and secondary endpoint, respectively). Conclusions: STE in aVR associated with STD in other leads predicts in-hospital CV death and culprit LMD. This pattern may add prognostic information in patients with NSTE-ACS on top of recommended scoring system.

The prediction of flash evaporation in superheated fuel injections for automotive applications

In gasoline Port Fuel Injection (PFI) and Direct Injection (GDI) internal combustion engines, the liquid fuel might be injected into a gaseous ambient in a superheated state, resulting in flash boiling of the fuel. The importance to investigate and predict such a process is due to the influence it has on the liquid fuel atomization and vaporization and thus on combustion, with direct implications on engine performances and exhaust gas emissions. The topic of the present PhD research involves the numerical analysis of the behaviour of the superheated fuel during the injection process, in high pressure injection systems like the ones equipping GDI engines. Particular emphasis is on the investigation of the effects of the fuel superheating degree on atomization dynamics and spray characteristics. The present work is a look at the flash evaporation and flash boiling modeling, from an engineering point of view, addressed to keep the complex physics involved as simple as possible, however capturing the main characteristics of a superheated fuel injection.

Changes in tumor stiffness for early prediction of tumor response to sorafenib: a proof-of-concept study with elastosonography in an animal model of Hepatocellular Carcinoma (HCC)

Background and aims: Sorafenib is the reference therapy for advanced Hepatocellular Carcinoma (HCC). No method exists to predict in the very early period subsequent individual response. Starting from the clinical experience in humans that subcutaneous metastases may rapidly change consistency under sorafenib and that elastosonography a new ultrasound based technique allows assessment of tissue stiffness, we investigated the role of elastonography in the very early prediction of tumor response to sorafenib in a HCC animal model. Methods: HCC (Huh7 cells) subcutaneous xenografting in mice was utilized. Mice were randomized to vehicle or treatment with sorafenib when tumor size was 5-10 mm. Elastosonography (Mylab 70XVG, Esaote, Genova, Italy) of the whole tumor mass on a sagittal plane with a 10 MHz linear transducer was performed at different time points from treatment start (day 0, +2, +4, +7 and +14) until mice were sacrified (day +14), with the operator blind to treatment. In order to overcome variability in absolute elasticity measurement when assessing changes over time, values were expressed in arbitrary units as relative stiffness of the tumor tissue in comparison to the stiffness of a standard reference stand-off pad lying on the skin over the tumor. Results: Sor-treated mice showed a smaller tumor size increase at day +14 in comparison to vehicle-treated (tumor volume increase +192.76% vs +747.56%, p=0.06). Among Sor-treated tumors, 6 mice showed a better response to treatment than the other 4 (increase in volume +177% vs +553%, p=0.011). At day +2, median tumor elasticity increased in Sor-treated group (+6.69%, range –30.17-+58.51%), while decreased in the vehicle group (-3.19%, range –53.32-+37.94%) leading to a significant difference in absolute values (p=0.034). From this time point onward, elasticity decreased in both groups, with similar speed over time, not being statistically different anymore. In Sor-treated mice all 6 best responders at day 14 showed an increase in elasticity at day +2 (ranging from +3.30% to +58.51%) in comparison to baseline, whereas 3 of the 4 poorer responders showed a decrease. Interestingly, these 3 tumours showed elasticity values higher than responder tumours at day 0. Conclusions: Elastosonography appears a promising non-invasive new technique for the early prediction of HCC tumor response to sorafenib. Indeed, we proved that responder tumours are characterized by an early increase in elasticity. The possibility to distinguish a priori between responders and non responders based on the higher elasticity of the latter needs to be validated in ad-hoc experiments as well as a confirmation of our results in humans is warranted.

A prediction of bone remodeling thanks to a mechanical signal on cells - Predizione del rimodellamento osseo a partire da un segnale meccanico sulle cellule

This text wants to explore the process of bone remodeling. The idea supported is that the signal, the cells acquire and which suggest them to change in their architectural conformation, is the potential difference on the free boundaries surfaces of collagen fibers. These ones represent the bone in the nanoscale. This work has as subject a multiscale model. Lots of studies have been made to try to discover the relationship between a macroscopic external bone load and the cellular scale. The tree first simulations have been a longitudinal, a flexion and a transversal compression force on a full longitudinal fiber 0-0 sample. The results showed first the great difference between a fully longitudinal stress and a flexion stress. Secondly a decrease in the potential difference has been observed in the transversal force configuration, suggesting that such a signal could be taken as the one, who leads the bone remodeling. To also exclude that the obtained results was not to attribute to a piezoelectric collagen effect and not to a mechanical load, different coupling analyses have been developed. Such analyses show this effect is really less important than the one the mechanical load is responsible of. At this point the work had to explore how bone remodeling could develop. The analyses involved different geometry and fibers percentage. Moreover at the beginning the model was to manually implement. The author, after an initial improvement of it, provided to implement a standalone version thanks to integration between Comsol Multiphysic, Matlab and Excel.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Subject-specific musculoskeletal models of the lower limbs for the prediction of skeletal loads during motion

The determination of skeletal loading conditions in vivo and their relationship to the health of bone tissues, remain an open question. Computational modeling of the musculoskeletal system is the only practicable method providing a valuable approach to muscle and joint loading analyses, although crucial shortcomings limit the translation process of computational methods into the orthopedic and neurological practice. A growing attention focused on subject-specific modeling, particularly when pathological musculoskeletal conditions need to be studied. Nevertheless, subject-specific data cannot be always collected in the research and clinical practice, and there is a lack of efficient methods and frameworks for building models and incorporating them in simulations of motion. The overall aim of the present PhD thesis was to introduce improvements to the state-of-the-art musculoskeletal modeling for the prediction of physiological muscle and joint loads during motion. A threefold goal was articulated as follows: (i) develop state-of-the art subject-specific models and analyze skeletal load predictions; (ii) analyze the sensitivity of model predictions to relevant musculotendon model parameters and kinematic uncertainties; (iii) design an efficient software framework simplifying the effort-intensive phases of subject-specific modeling pre-processing. The first goal underlined the relevance of subject-specific musculoskeletal modeling to determine physiological skeletal loads during gait, corroborating the choice of full subject-specific modeling for the analyses of pathological conditions. The second goal characterized the sensitivity of skeletal load predictions to major musculotendon parameters and kinematic uncertainties, and robust probabilistic methods were applied for methodological and clinical purposes. The last goal created an efficient software framework for subject-specific modeling and simulation, which is practical, user friendly and effort effective. Future research development aims at the implementation of more accurate models describing lower-limb joint mechanics and musculotendon paths, and the assessment of an overall scenario of the crucial model parameters affecting the skeletal load predictions through probabilistic modeling.

Microemulsion: prediction of the phase diagram with a modified Helfrich free energy

Microemulsions are thermodynamically stable, macroscopically homogeneous but microscopically heterogeneous, mixtures of water and oil stabilised by surfactant molecules. They have unique properties like ultralow interfacial tension, large interfacial area and the ability to solubilise other immiscible liquids. Depending on the temperature and concentration, non-ionic surfactants self assemble to micelles, flat lamellar, hexagonal and sponge like bicontinuous morphologies. Microemulsions have three different macroscopic phases (a) 1phase- microemulsion (isotropic), (b) 2phase-microemulsion coexisting with either expelled water or oil and (c) 3phase- microemulsion coexisting with expelled water and oil.rnrnOne of the most important fundamental questions in this field is the relation between the properties of the surfactant monolayer at water-oil interface and those of microemulsion. This monolayer forms an extended interface whose local curvature determines the structure of the microemulsion. The main part of my thesis deals with the quantitative measurements of the temperature induced phase transitions of water-oil-nonionic microemulsions and their interpretation using the temperature dependent spontaneous curvature [c0(T)] of the surfactant monolayer. In a 1phase- region, conservation of the components determines the droplet (domain) size (R) whereas in 2phase-region, it is determined by the temperature dependence of c0(T). The Helfrich bending free energy density includes the dependence of the droplet size on c0(T) as

Prediction of new crystal structures under extreme conditions

One of the most important challenges in chemistry and material science is the connection between the contents of a compound and its chemical and physical properties. In solids, these are greatly influenced by the crystal structure.rnrnThe prediction of hitherto unknown crystal structures with regard to external conditions like pressure and temperature is therefore one of the most important goals to achieve in theoretical chemistry. The stable structure of a compound is the global minimum of the potential energy surface, which is the high dimensional representation of the enthalpy of the investigated system with respect to its structural parameters. The fact that the complexity of the problem grows exponentially with the system size is the reason why it can only be solved via heuristic strategies.rnrnImprovements to the artificial bee colony method, where the local exploration of the potential energy surface is done by a high number of independent walkers, are developed and implemented. This results in an improved communication scheme between these walkers. This directs the search towards the most promising areas of the potential energy surface.rnrnThe minima hopping method uses short molecular dynamics simulations at elevated temperatures to direct the structure search from one local minimum of the potential energy surface to the next. A modification, where the local information around each minimum is extracted and used in an optimization of the search direction, is developed and implemented. Our method uses this local information to increase the probability of finding new, lower local minima. This leads to an enhanced performance in the global optimization algorithm.rnrnHydrogen is a highly relevant system, due to the possibility of finding a metallic phase and even superconductor with a high critical temperature. An application of a structure prediction method on SiH12 finds stable crystal structures in this material. Additionally, it becomes metallic at relatively low pressures.

Analytical model for the prediction of wrinkling of rear pressure bulkheads

In Airbus GmbH (Hamburg) has been developed a new design of Rear Pressure Bulkhead (RPB) for the A320-family. The new model has been formed with vacuum forming technology. During this process the wrinkling phenomenon occurs. In this thesis is described an analytical model for prediction of wrinkling based on the energetic method of Timoshenko. Large deflection theory has been used for analyze two cases of study: a simply supported circular thin plate stamped by a spherical punch and a simply supported circular thin plate formed with vacuum forming technique. If the edges are free to displace radially, thin plates will develop radial wrinkles near the edge at a central deflection approximately equal to four plate thicknesses w0/ℎ≈4 if they’re stamped by a spherical punch and w0/ℎ≈3 if they’re formed with vacuum forming technique. Initially, there are four symmetrical wrinkles, but the number increases if the central deflection is increased. By using experimental results, the “Snaptrhough” phenomenon is described.

On the recurrence of random walks in Lévy random environments

This thesis investigates one-dimensional random walks in random environment whose transition probabilities might have an infinite variance. The ergodicity of the dynamical system ''from the point of view of the particle'' is proved under the assumptions of transitivity and existence of an absolutely continuous steady state on the space of the environments. We show that, if the average of the local drift over the environments is summable and null, then the RWRE is recurrent. We provide an example satisfying all the hypotheses.

Prediction of dental implant torque with a fast and automatic finite element analysis: a pilot study

Despite its importance, implant removal torque can be assessed at present only after implantation. This paper presents a new technique to help clinicians preoperatively evaluate implant stability.

Prediction of hip and hand fractures in older persons with or without a diagnosis of periodontitis

Purpose: In a prospective study, we assessed if a diagnosis of osteoporosis and periodontitis could predict hip and hand fractures in older persons. Materials and methods: Bone density was assessed by a Densitometer. Periodontitis was defined by evidence of alveolar bone loss. Results: 788 Caucasians (52.4% women, overall mean age: 76 years, S.D. +/- 9.0, range: 62 to 96) were enrolled and 7.4% had a hip/hand fracture in 3 years. Calcaneus PIXI T-values < - 1.6 identified osteoporosis in 28.2% of the older persons predicting a hip/hand fracture with an odds ratio of 3.3:1 (95% CI: 1.9, 5.7, p < 0.001). Older persons with osteoporosis had more severe periodontitis (p < 0.01). Periodontitis defined by >= 30% of sites with >= 5 mm distance between the cemento-enamel junction (CEJ) and bone level (ABL) was found in 18.7% of the older persons predicting a hip/hand fracture with an odds ratio of 1.8:1 (95% CI: 1.0, 3.3, p < 0.05). Adjusted for age, the odds ratio of a hip/hand fracture in older persons with osteoporosis (PIXI T-value <-2.5) and periodontitis was 12.2:1 (95% CI: 3.5, 42.3, p < 0.001). Conclusions: Older persons with osteoporosis and periodontitis have an increased risk for hip/hand fractures

The role of rotation thromboelastometry in early prediction of massive transfusion

Early prediction of massive transfusion (MT) is critical in the management of severely injured trauma patients. Variables available early after injury including physiologic, laboratory, and rotation thromboelastometric (ROTEM) parameters were evaluated as predictors for the need of MT.

Dysphonic newborn cries allow prediction of their perceived meaning

Can adults attribute different meanings to the cries produced by the newborns on the basis of physical-acoustic cues in different communication conditions? In order to test this hypothesis, 20 females were asked to evaluate (according to four scales: anguish, anger, annoyance, care-seeking) 24 cries from 12 healthy full-term newborns (4-7h old): 6 newborns previously exposed to tactile communication (Group 1); 6 newborns without communication (Group 2). Annoyance category was not reliable and it was excluded from analyses. The 20 females attributed a higher rate of anger and anguish to the cries from Group 2, and a higher rate of care-seeking to the cries from Group 1. They attributed different meanings to the cries from Group 1, and undifferentiated meanings to the cries from Group 2. Consistent with bivariate analyses, Dysphonic Cry was the strongest predictor of anger/anguish. Although the Hyperphonic Cry was quantitatively not relevant, its absence was the first predictor for care-seeking.