Uterine Doppler velocimetry of the uterine arteries in the second and third trimesters for the prediction of gestational outcome

PURPOSE: The aim of this longitudinal study was to investigate the value of uterine artery Doppler sonography during the second and third trimesters in the prediction of adverse pregnancy outcome in low-risk women. METHODS: From July 2011 to August 2012, a total of 205 singleton pregnant women presenting at our antenatal clinic were enrolled in this prospective study and were assessed for baseline demographic and obstetric data. They underwent ultrasound evaluation at the time of second and third trimesters, both included Doppler assessment of bilateral uterine arteries to determine the values of the pulsatility index (PI) and resistance index (RI) and presence of early diastolic notch. The endpoint of this study was assessing the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of Doppler ultrasonography of the uterine artery, for the prediction of adverse pregnancy outcomes including preeclampsia, stillbirth, placental abruption and preterm labor. RESULTS: The mean age of cases was 26.4±5.11. The uterine artery PI and RI values for both second (PI: 1.1±0.42 versus 1.53±0.59, p=0.002; RI: 0.55±0.09 versus 0.72±0.13, p=0.000 respectively) and third-trimester (PI: 0.77±0.31 versus 1.09±0.46, p=0.000; RI: 0.46±0.10 versus 0.60±0.14, p=0.010 respectively) evaluations were significantly higher in patients with adverse pregnancy outcome than in normal women. Combination of PI and RI >95th percentile and presence of bilateral notch in second trimester get sensitivity and specificity of 36.1 and 97% respectively, while these measures were 57.5 and 98.2% in third trimester. CONCLUSIONS: According to our study, it seems that uterine artery Doppler may be a valuable tool for the prediction of a variety of adverse outcomes in second and third trimesters.

Criteria for prediction of metastatic axillary lymph nodes in early-stage breast cancer

PURPOSE: To estimate the likelihood of axillary lymph node involvement for patients with early-stage breast cancer, based on a variety of clinical and pathological factors. METHODS: A retrospective analysis was done in hospital databases from 1999 to 2007. Two hundred thirty-nine patients were diagnosed with early-stage breast cancer. Predictive factors, such as patient age, tumor size, lymphovascular invasion, histological grade and immunohistochemical subtype were analyzed to identify variables that may be associated with axillary lymph node metastasis. RESULTS: Patients with tumors that are negative for estrogen receptor, progesterone receptor, and HER2 had approximately a 90% lower chance of developing lymph node metastasis than those with luminal A tumors (e.g., ER+ and/or PR+ and HER2-) - Odds Ratio: 0.11; 95% confidence interval: 0.01-0.88; p=0.01. Furthermore, the risk for lymph node metastasis of luminal A tumors seemed to decrease as patient age increased, and it was directly correlated with tumor size. CONCLUSION: The molecular classification of early-stage breast cancer using immunohistochemistry may help predicting the probability of developing axillary lymph node metastasis. Further studies are needed to optimize predictions for nodal involvement, with the aim of aiding the decision-making process for breast cancer treatment.

Prediction of 3D structure and ligand-binding properties : ABC transporters and flavodiiron proteins from Synechocystis sp. strain PCC 6803

Cyanobacteria are unicellular, non-nitrogen-fixing prokaryotes, which perform photosynthesis similarly as higher plants. The cyanobacterium Synechocystis sp. strain PCC 6803 is used as a model organism in photosynthesis research. My research described herein aims at understanding the function of the photosynthetic machinery and how it responds to changes in the environment. Detailed knowledge of the regulation of photosynthesis in cyanobacteria can be utilized for biotechnological purposes, for example in the harnessing of solar energy for biofuel production. In photosynthesis, iron participates in electron transfer. Here, we focused on iron transport in Synechocystis sp. strain PCC 6803 and particularly on the environmental regulation of the genes encoding the FutA2BC ferric iron transporter, which belongs to the ABC transporter family. A homology model built for the ATP-binding subunit FutC indicates that it has a functional ATPbinding site as well as conserved interactions with the channel-forming subunit FutB in the transporter complex. Polyamines are important for the cell proliferation, differentiation and apoptosis in prokaryotic and eukaryotic cells. In plants, polyamines have special roles in stress response and in plant survival. The polyamine metabolism in cyanobacteria in response to environmental stress is of interest in research on stress tolerance of higher plants. In this thesis, the potd gene encoding an polyamine transporter subunit from Synechocystis sp. strain PCC 6803 was characterized for the first time. A homology model built for PotD protein indicated that it has capability of binding polyamines, with the preference for spermidine. Furthermore, in order to investigate the structural features of the substrate specificity, polyamines were docked into the binding site. Spermidine was positioned very similarly in Synechocystis PotD as in the template structure and had most favorable interactions of the docked polyamines. Based on the homology model, experimental work was conducted, which confirmed the binding preference. Flavodiiron proteins (Flv) are enzymes, which protect the cell against toxicity of oxygen and/or nitric oxide by reduction. In this thesis, we present a novel type of photoprotection mechanism in cyanobacteria by the heterodimer of Flv2/Flv4. The constructed homology model of Flv2/Flv4 suggests a functional heterodimer capable of rapid electron transfer. The unknown protein sll0218, encoded by the flv2-flv4 operon, is assumed to facilitate the interaction of the Flv2/Flv4 heterodimer and energy transfer between the phycobilisome and PSII. Flv2/Flv4 provides an alternative electron transfer pathway and functions as an electron sink in PSII electron transfer.

Prediction of transients and control reactions in a transonic wind tunnel

This work describes a lumped parameter mathematical model for the prediction of transients in an aerodynamic circuit of a transonic wind tunnel. Control actions to properly handle those perturbations are also assessed. The tunnel circuit technology is up to date and incorporates a novel feature: high-enthalpy air injection to extend the tunnel’s Reynolds number capability. The model solves the equations of continuity, energy and momentum and defines density, internal energy and mass flow as the basic parameters in the aerodynamic study as well as Mach number, stagnation pressure and stagnation temperature, all referred to test section conditions, as the main control variables. The tunnel circuit response to control actions and the stability of the flow are numerically investigated. Initially, for validation purposes, the code was applied to the AWT ("Altitude Wind Tunnel" of NASA-Lewis). In the sequel, the Brazilian transonic wind tunnel was investigated, with all the main control systems modeled, including injection.

Linear prediction in optical spectroscopy

A linear prediction procedure is one of the approved numerical methods of signal processing. In the field of optical spectroscopy it is used mainly for extrapolation known parts of an optical signal in order to obtain a longer one or deduce missing signal samples. The first is needed particularly when narrowing spectral lines for the purpose of spectral information extraction. In the present paper the coherent anti-Stokes Raman scattering (CARS) spectra were under investigation. The spectra were significantly distorted by the presence of nonlinear nonresonant background. In addition, line shapes were far from Gaussian/Lorentz profiles. To overcome these disadvantages the maximum entropy method (MEM) for phase spectrum retrieval was used. The obtained broad MEM spectra were further underwent the linear prediction analysis in order to be narrowed.

Prediction of market switching and delisting events from OMX First North Nordic multilateral stock exchange

This thesis studies the predictability of market switching and delisting events from OMX First North Nordic multilateral stock exchange by using financial statement information and market information from 2007 to 2012. This study was conducted by using a three stage process. In first stage relevant theoretical framework and initial variable pool were constructed. Then, explanatory analysis of the initial variable pool was done in order to further limit and identify relevant variables. The explanatory analysis was conducted by using self-organizing map methodology. In the third stage, the predictive modeling was carried out with random forests and support vector machine methodologies. It was found that the explanatory analysis was able to identify relevant variables. The results indicate that the market switching and delisting events can be predicted in some extent. The empirical results also support the usability of financial statement and market information in the prediction of market switching and delisting events.

Warranty Prediction Using Weibull Simulation

The main objective of this master’s thesis is to examine if Weibull analysis is suitable method for warranty forecasting in the Case Company. The Case Company has used Reliasoft’s Weibull++ software, which is basing on the Weibull method, but the Company has noticed that the analysis has not given right results. This study was conducted making Weibull simulations in different profit centers of the Case Company and then comparing actual cost and forecasted cost. Simula-tions were made using different time frames and two methods for determining future deliveries. The first sub objective is to examine, which parameters of simulations will give the best result to each profit center. The second sub objective of this study is to create a simple control model for following forecasted costs and actual realized costs. The third sub objective is to document all Qlikview-parameters of profit centers. This study is a constructive research, and solutions for company’s problems are figured out in this master’s thesis. In the theory parts were introduced quality issues, for example; what is quality, quality costing and cost of poor quality. Quality is one of the major aspects in the Case Company, so understand-ing the link between quality and warranty forecasting is important. Warranty management was also introduced and other different tools for warranty forecasting. The Weibull method and its mathematical properties and reliability engineering were introduced. The main results of this master’s thesis are that the Weibull analysis forecasted too high costs, when calculating provision. Although, some forecasted values of profit centers were lower than actual values, the method works better for planning purposes. One of the reasons is that quality improving or alternatively quality decreasing is not showing in the results of the analysis in the short run. The other reason for too high values is that the products of the Case Company are com-plex and analyses were made in the profit center-level. The Weibull method was developed for standard products, but products of the Case Company consists of many complex components. According to the theory, this method was developed for homogeneous-data. So the most im-portant notification is that the analysis should be made in the product level, not the profit center level, when the data is more homogeneous.

Early prediction of poor outcome in patients with acute asthma in the emergency room

Early identification of patients who need hospitalization or patients who should be discharged would be helpful for the management of acute asthma in the emergency room. The objective of the present study was to examine the clinical and pulmonary functional measures used during the first hour of assessment of acute asthma in the emergency room in order to predict the outcome. We evaluated 88 patients. The inclusion criteria were age between 12 and 55 years, forced expiratory volume in the first second below 50% of predicted value, and no history of chronic disease or pregnancy. After baseline evaluation, all patients were treated with 2.5 mg albuterol delivered by nebulization every 20 min in the first hour and 60 mg of intravenous methylprednisolone. Patients were reevaluated after 60 min of treatment. Sixty-five patients (73.9%) were successfully treated and discharged from the emergency room (good responders), and 23 (26.1%) were hospitalized or were treated and discharged with relapse within 10 days (poor responders). A predictive index was developed: peak expiratory flow rates after 1 h <=0% of predicted values and accessory muscle use after 1 h. The index ranged from 0 to 2. An index of 1 or higher presented a sensitivity of 74.0, a specificity of 69.0, a positive predictive value of 46.0, and a negative predictive value of 88.0. It was possible to predict outcome in the first hour of management of acute asthma in the emergency room when the index score was 0 or 2.

Prediction of exposed domains of envelope glycoprotein in Indian HIV-1 isolates and experimental confirmation of their immunogenicity in humans

We describe the impact of subtype differences on the seroreactivity of linear antigenic epitopes in envelope glycoprotein of HIV-1 isolates from different geographical locations. By computer analysis, we predicted potential antigenic sites of envelope glycoprotein (gp120 and gp4l) of this virus. For this purpose, after fetching sequences of proteins of interest from data banks, values of hydrophilicity, flexibility, accessibility, inverted hydrophobicity, and secondary structure were considered. We identified several potential antigenic epitopes in a B subtype strain of envelope glycoprotein of HIV-1 (IIIB). Solid- phase peptide synthesis methods of Merrifield and Fmoc chemistry were used for synthesizing peptides. These synthetic peptides corresponded mainly to the C2, V3 and CD4 binding sites of gp120 and some parts of the ectodomain of gp41. The reactivity of these peptides was tested by ELISA against different HIV-1-positive sera from different locations in India. For two of these predicted epitopes, the corresponding Indian consensus sequences (LAIERYLKQQLLGWG and DIIGDIRQAHCNISEDKWNET) (subtype C) were also synthesized and their reactivity was tested by ELISA. These peptides also distinguished HIV-1-positive sera of Indians with C subtype infections from sera from HIV-negative subjects.

Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Utility of a novel risk score for prediction of ventricular tachycardia and cardiac death in chronic Chagas disease - the SEARCH-RIO study

The SEARCH-RIO study prospectively investigated electrocardiogram (ECG)-derived variables in chronic Chagas disease (CCD) as predictors of cardiac death and new onset ventricular tachycardia (VT). Cardiac arrhythmia is a major cause of death in CCD, and electrical markers may play a significant role in risk stratification. One hundred clinically stable outpatients with CCD were enrolled in this study. They initially underwent a 12-lead resting ECG, signal-averaged ECG, and 24-h ambulatory ECG. Abnormal Q-waves, filtered QRS duration, intraventricular electrical transients (IVET), 24-h standard deviation of normal RR intervals (SDNN), and VT were assessed. Echocardiograms assessed left ventricular ejection fraction. Predictors of cardiac death and new onset VT were identified in a Cox proportional hazard model. During a mean follow-up of 95.3 months, 36 patients had adverse events: 22 new onset VT (mean±SD, 18.4±4‰/year) and 20 deaths (26.4±1.8‰/year). In multivariate analysis, only Q-wave (hazard ratio, HR=6.7; P<0.001), VT (HR=5.3; P<0.001), SDNN<100 ms (HR=4.0; P=0.006), and IVET+ (HR=3.0; P=0.04) were independent predictors of the composite endpoint of cardiac death and new onset VT. A prognostic score was developed by weighting points proportional to beta coefficients and summing-up: Q-wave=2; VT=2; SDNN<100 ms=1; IVET+=1. Receiver operating characteristic curve analysis optimized the cutoff value at >1. In 10,000 bootstraps, the C-statistic of this novel score was non-inferior to a previously validated (Rassi) score (0.89±0.03 and 0.80±0.05, respectively; test for non-inferiority: P<0.001). In CCD, surface ECG-derived variables are predictors of cardiac death and new onset VT.

HIGH PRESSURE PHASE EQUILIBRIUM: PREDICTION OF ESSENTIAL OIL SOLUBILITY

This work describes a method to predict the solubility of essential oils in supercritical carbon dioxide. The method is based on the formulation proposed in 1979 by Asselineau, Bogdanic and Vidal. The Peng-Robinson and Soave-Redlich-Kwong cubic equations of state were used with the van der Waals mixing rules with two interaction parameters. Method validation was accomplished calculating orange essential oil solubility in pressurized carbon dioxide. The solubility of orange essential oil in carbon dioxide calculated at 308.15 K for pressures of 50 to 70 bar varied from 1.7± 0.1 to 3.6± 0.1 mg/g. For same the range of conditions, experimental solubility varied from 1.7± 0.1 to 3.6± 0.1 mg/g. Predicted values were not very sensitive to initial oil composition.

Application of mathematical models for the prediction of adsorption isotherms in solid mixture for mango powder refreshment

Solid mixtures for refreshment are already totally integrated to the Brazilian consumers' daily routine, because of their quick preparation method, yield and reasonable price - quite lower if compared to 'ready-to-drink' products or products for prompt consumption, what makes them economically more accessible to low-income populations. Within such a context, the aim of this work was to evaluate the physicochemical and mineral composition, as well as the hygroscopic behavior of four different brands of solid mixture for mango refreshment. The BET, GAB, Oswim and Henderson mathematical models were built through the adjustment of experimental data to the isotherms of adsorption. Results from the physiochemical evaluation showed that the solid mixtures for refreshments are considerable sources of ascorbic acid and reductor sugar; and regarding mineral compounds, they are significant sources of calcium, sodium and potassium. It was also verified that the solid mixtures for refreshments of the four studied brands are considered highly hygroscopic.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.