Resistance of Raphanus raphanistrum to the herbicide metsulfuron-methyl

The ALS-inhibiting herbicides, especially metsulfuron-methyl, are widely used for weed control, mainly wheat and barley in southern Brazil. Raphanus raphanistrum is a major weed of winter crops. However, in recent years, R.raphanistrum, after being treated with metsulfuron, has shown no symptoms of toxicity, possibly due to herbicide resistance. Aiming to evaluate the existence of R.raphanistrum biotypes resistant to metsulfuron, an experiment was conducted in a greenhouse, in a completely randomized design with four replications. The plots consisted of pots with six plants. The treatments consisted of the interaction of resistant R. raphanistrum (biotype R) and susceptible R. raphanistrum (biotypes S) with ten doses of the herbicide (0.0; 0.6; 1.2; 2.4; 4.8; 9.6; 19.2; 38.4; 76.8 and 153.6 g i.a. ha-1). The application of the test herbicides occurred when the crop was at the stage of 3 to 4 true leaves. The variables analyzed were control and dry matter accumulation. Statistical analysis of dose-response curves was performed by non linear regression. Biotype S was susceptible to the herbicide even at doses below the recommended. Biotype R was insensitive to the herbicide obtaining values of resistance factor (F) higher than 85. The dose-response curve confirmed the existence of R. raphanistrum biotypes with high level of resistance to metsulfuron-methyl.

Wheat Yield Loss in a Two Species Competition with Emex australis and Emex spinosa

Emex australis and E. spinosa are significant weed species in wheat and other crops. Information on the extent of competition of the Emex species will be helpful to access yield losses in wheat. Field experiments were conducted to quantify the interference of tested weed densities each as single or mixture of both at 1:1 on their growth and yield, wheat yield components and wheat grain yield losses in two consecutive years. Dry weight of both weed species increased from 3-6 g m-2 with every additional plant of weed, whereas seed number and weight per plant decreased with increasing density of either weed. Both weed species caused considerable decrease in yield components like spike bearing tillers, number of grains per spike, 1000-grain weight of wheat with increasing density population of the weeds. Based on non-linear hyperbolic regression model equation, maximum yield loss at asymptotic weed density was estimated to be 44 and 62% with E. australis, 56 and 70% with E. spinosa and 63 and 72% with mixture of both species at 1:1 during both year of study, respectively. It was concluded that E. spinosa has more competition effects on wheat crop as compared to E. australis.

An overview of lignin metabolism and its effect on biomass recalcitrance

Lignin, after cellulose, is the second most abundant biopolymer on Earth, accounting for 30% of the organic carbon in the biosphere. It is considered an important evolutionary adaptation of plants during their transition from the aquatic environment to land, since it bestowed the early tracheophytes with physical support to stand upright and enabled long-distance transport of water and solutes by waterproofing the vascular tissue. Although essential for plant growth and development, lignin is the major plant cell wall component responsible for biomass recalcitrance to industrial processing. The fact that lignin is a non-linear aromatic polymer built with chemically diverse and poorly reactive linkages and a variety of monomer units precludes the ability of any single enzyme to properly recognize and degrade it. Consequently, the use of lignocellulosic feedstock as a renewable and sustainable resource for the production of biofuels and bio-based materials will depend on the identification and characterization of the factors that determine plant biomass recalcitrance, especially the highly complex phenolic polymer lignin. Here, we summarize the current knowledge regarding lignin metabolism in plants, its effect on biomass recalcitrance and the emergent strategies to modify biomass recalcitrance through metabolic engineering of the lignin pathway. In addition, the potential use of sugarcane as a second-generation biofuel crop and the advances in lignin-related studies in sugarcane are discussed.

Altered heart rate and blood pressure variability in mice lacking the Mas protooncogene

Heart rate variability is a relevant predictor of cardiovascular risk in humans. A significant genetic influence on heart rate variability is suggested, although the genes involved are ill-defined. The Mas-protooncogene encodes a G-protein-coupled receptor with seven transmembrane domains highly expressed in testis and brain. Since this receptor is supposed to interact with the signaling of angiotensin II, which is an important regulator of cardiovascular homeostasis, heart rate and blood pressure were analyzed in Mas-deficient mice. Using a femoral catheter the blood pressure of mice was measured for a period of 30 min and 250 data values per second were recorded. The mean values and range of heart rate and blood pressure were then calculated. Neither heart rate nor blood pressure were significantly different between knockout mice and controls. However, high resolution recording of these parameters and analysis of the data by non-linear dynamics revealed significant alterations in cardiovascular variability in Mas-deficient animals. In particular, females showed a strong reduction of heart rate variability. Furthermore, the data showed an increased sympathetic tone in knockout animals of both genders. The marked alterations detected in Mas-deficient mice of both genders suggest that the Mas-protooncogene is an important determinant of heart rate and blood pressure variability.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Osinkosuhteen, osinkotuoton ja omavaraisuusasteen sijoitusstrateginen hyödynnettävyys Suomen osakemarkkinoilla

Tutkielman tavoitteena on selvittää osinkosuhteen, osinkotuoton ja omavaraisuusasteen vaikutus osakkeesta saatavaan kokonaistuottoon Suomenosakemarkkinoilla vuosina 2002–2013. Muuttujien kausaliteettisuhde kokonaistuottoon selvitetään regressioanalyysilla. Portfolioanalyysin avulla tutkitaan valittujen tunnuslukujen toimivuutta sijoitusstrategiana. Tutkimuksessa muodostetaan myös osinkosuhteen ja osinkotuoton yhdistelmänä tunnusluku, jolla pyritään maksimoimaan sijoittajan saama tuotto. Empiiriset tulokset osoittivat, että sijoittaja pystyy saavuttamaan ylituottoja hyödyntämällä edellä mainittuja tunnuslukuja osakevalinnassa. Osinkotuoton ja osakkeen kokonaistuoton välillä havaittiin positiivinen lineaarinen korrelaatio. Portfolioanalyysin perusteella sekä omavaraisuusasteen että osinkosuhteen osalta vaikutus sijoittajan saamaan riskisuhteutettuun kokonaistuottoon on ei-lineaarinen. Valittuja tunnuslukuja ja menetelmiä hyödyntäen sijoittaja saa parhaimman riskisuhteutetun tuoton valitsemalla sijoitussalkkuunsa osakkeita, joiden osinkosuhteen arvo sijoittuu toiseksi ylimpään kvartiiliin sekä osakkeita, joiden osinkotuotto on korkea ja omavaraisuusaste on samanaikaisesti alhainen.

Toward including the effect of manufacturing processes in the pre-estimated losses of the switched reluctance motor

The estimation of losses plays a key role in the process of building any electrical machine. How to estimate those losses while designing any machine; by obtaining the characteristic of the electrical steel from the catalogue and calculate the losses. However, this way is inaccurate since the electrical steel performs several manufacturing processes during the process of building any machine, which affects directly the magnetic property of the electrical steel and accordingly the characteristic of the electrical steel will be affected. That means the B–H curve of the steel that was obtained from the catalogue will be changed. Moreover, during loading and rotating the machine, some important changes occur to the B–H characteristic of the electrical steel such as the stress on the laminated iron. Accordingly, the pre-estimated losses are completely far from the actual losses because they were estimated based on the data of the electrical steel obtained from the catalogue. So in order to estimate the losses precisely significant factors of the manufacturing processes must be included. The paper introduces the systematic estimation of the losses including the effect of one of the manufacturing factors. Similarly, any other manufacturing factor can be included in the pre-designed losses estimations.

Modeling a Network of Heat Exchangers

This research work addresses the problem of building a mathematical model for the given system of heat exchangers and to determine the temperatures, pressures and velocities at the intermediate positions. Such model could be used in nding an optimal design for such a superstructure. To limit the size and computing time a reduced network model was used. The method can be generalized to larger network structures. A mathematical model which includes a system of non-linear equations has been built and solved according to the Newton-Raphson algorithm. The results obtained by the proposed mathematical model were compared with the results obtained by the Paterson approximation and Chen's Approximation. Results of this research work in collaboration with a current ongoing research at the department will optimize the valve positions and hence, minimize the pumping cost and maximize the heat transfer of the system of heat exchangers.

Spectral and symbolic analysis of the effect of gender and postural change on cardiac autonomic modulation in healthy elderly subjects

The objective of this study was to use linear and non-linear methods to investigate cardiac autonomic modulation in healthy elderly men and women in response to a postural change from the supine to the standing position. Fourteen men (66.1 ± 3.5 years) and 10 women (65.3 ± 3.3 years) were evaluated. Beat-to-beat heart rate was recorded in the supine and standing positions. Heart rate variability was studied by spectral analysis, including both low (LFnu-cardiac sympathetic modulation (CSM) indicator) and high (HFnu-cardiac vagal modulation (CVM) indicator) frequencies in normalized units as well as the low frequency/high frequency (LF/HF) ratio. Symbolic analysis was performed using the following indexes: 0V% (CSM indicator), 1V% (CSM and CVM indicators), 2LV% (predominantly CVM indicator) and 2ULV% (CVM indicator). Shannon entropy was also calculated. Men presented higher LFnu and LF/HF ratio and lower HFnu and 1V% symbolic index (57.56, 4.14, 40.53, 45.96, respectively) than women (24.60, 0.45, 72.47, 52.69, respectively) in the supine position. Shannon entropy was higher among men (3.53) than among women (3.33) in the standing position, and also increased according to postural change in men (3.25; 3.53). During postural change, the LFnu (24.60; 49.85) and LF/HF ratio (0.45; 1.72) increased, with a concomitant decrease in HFnu (72.47; 47.56) and 2LV% (14.10; 6.95) in women. Women presented increased CSM in response to postural change and had higher CVM and lower CSM than men in the supine position. In conclusion, women in the age range studied presented a more appropriate response to a postural change than men, suggesting that cardiac autonomic modulation may be better preserved in women than in men.

Cardiac autonomic modulation during progressive upper limb exercise by patients with coronary artery disease

The purpose of this study was to investigate the behavior of heart rate (HR) and HR variability (HRV) during different loads of resistance exercise (incline bench press) in patients with coronary artery disease (CAD) and healthy sedentary controls. Ten healthy men (65 ± 1.2 years, control group, CG) and 10 men with clinically stable CAD (66 ± 2.4 years, CADG) were recruited. A discontinuous progressive protocol was applied with an initial load of 10% of the maximum load achieved in the 1RM (1 repetition maximum) with increases of 10% until 30% 1RM was reached, which was followed by subsequent increases of 5% 1RM until exhaustion. HRV was analyzed by linear and non-linear methods. There was a significant reduction in rMSSD (CG: 20 ± 2 to 11 ± 3 ms; CADG: 19 ± 3 to 9 ± 1 ms) and SD1 indexes (CG: 14 ± 2 to 8 ± 1 ms; CADG: 14 ± 2 to 7 ± 1 ms). An increase in HR (CG: 69 ± 5 to 90 ± 5 bpm; CADG: 62 ± 4 to 75 ± 4 bpm) and in systolic blood pressure (CG: 124 ± 3 to 138 ± 3 mmHg; CADG: 122 ± 6 to 126 ± 9 bpm) were observed (P < 0.05) when comparing pre-effort rest and 40% 1RM in both groups. Furthermore, an increase in RMSM index was also observed (CG: 28 ± 3 to 45 ± 9 ms; CADG: 22 ± 2 to 79 ± 33 ms), with higher values in CADG. We conclude that loads up to 30% 1RM during incline bench press result in depressed vagal modulation in both groups, although only stable CAD patients presented sympathetic overactivity at 20% 1RM upper limb exercise.

Spectrally Tuned Lanthanide Photoluminescence for Point-of-Care Testing

Point-of-care (POC) –diagnostics is a field with rapidly growing market share. As these applications become more widely used, there is an increasing pressure to improve their performance to match the one of a central laboratory tests. Lanthanide luminescence has been widely utilized in diagnostics because of the numerous advantages gained by the utilization of time-resolved or anti-Stokes detection. So far the use of lanthanide labels in POC has been scarce due to limitations set by the instrumentation required for their detection and the shortcomings, e.g. low brightness, of these labels. Along with the advances in the research of lanthanide luminescence, and in the field of semiconductors, these materials are becoming a feasible alternative for the signal generation also in the future POC assays. The aim of this thesis was to explore ways of utilizing time-resolved detection or anti-Stokes detection in POC applications. The long-lived fluorescence for the time-resolved measurement can be produced with lanthanide chelates. The ultraviolet (UV) excitation required by these chelates is cumbersome to produce with POC compatible fluorescence readers. In this thesis the use of a novel light-harvesting ligand was studied. This molecule can be used to excite Eu(III)-ions at wavelengths extending up to visible part of the spectrum. An enhancement solution based on this ligand showed a good performance in a proof-of-concept -bioaffinity assay and produced a bright signal upon 365 nm excitation thanks to the high molar absorptivity of the chelate. These features are crucial when developing miniaturized readers for the time-resolved detection of fluorescence. Upconverting phosphors (UCPs) were studied as an internal light source in glucose-sensing dry chemistry test strips and ways of utilizing their various emission wavelengths and near-infrared excitation were explored. The use of nanosized NaYF :Yb3+,Tm3+-particles enabled the replacement of an external UV-light source with a NIR-laser and gave an additional degree of freedom in the optical setup of the detector instrument. The new method enabled a blood glucose measurement with results comparable to a current standard method of measuring reflectance. Microsized visible emitting UCPs were used in a similar manner, but with a broad absorbing indicator compound filtering the excitation and emission wavelengths of the UCP. This approach resulted in a novel way of benefitting from the non-linear relationship between the excitation power and emission intensity of the UCPs, and enabled the amplification of the signal response from the indicator dye.

Optimizing the Performance of a Heat Exchanger Network

This research is the continuation and a joint work with a master thesis that has been done in this department recently by Hemamali Chathurangani Yashika Jayathunga. The mathematical system of the equations in the designed Heat Exchanger Network synthesis has been extended by adding a number of equipment; such as heat exchangers, mixers and dividers. The solutions of the system is obtained and the optimal setting of the valves (Each divider contains a valve) is calculated by introducing grid-based optimization. Finding the best position of the valves will lead to maximization of the transferred heat in the hot stream and minimization of the pressure drop in the cold stream. The aim of the following thesis will be achieved by practicing the cost optimization to model an optimized network.

Os efeitos da política educacional no crescimento econômico: teoria e estimativas dinâmicas em painel de dados

In this paper, we develop a theoretical model that considers a non-linear relationship between growth and level of education (human capital). Our econometric estimates demonstrated the causality running from human capital to GDP per capita with U inverted shape. The level of education (human capital) that generates the maximum growth rate lies around 4.5 years. The foremost implication of this result is that States with level of education below this range should have as priority educational policies.

Modelling the exothermic heat and the demand of cooling in paper and pulp industry biological wastewater treatment

This master thesis presents a study on the requisite cooling of an activated sludge process in paper and pulp industry. The energy consumption of paper and pulp industry and it’s wastewater treatment plant in particular is relatively high. It is therefore useful to understand the wastewater treatment process of such industries. The activated sludge process is a biological mechanism which degrades carbonaceous compounds that are present in waste. The modified activated sludge model constructed here aims to imitate the bio-kinetics of an activated sludge process. However, due to the complicated non-linear behavior of the biological process, modelling this system is laborious and intriguing. We attempt to find a system solution first using steady-state modelling of Activated Sludge Model number 1 (ASM1), approached by Euler’s method and an ordinary differential equation solver. Furthermore, an enthalpy study of paper and pulp industry’s vital pollutants was carried out and applied to revise the temperature shift over a period of time to formulate the operation of cooling water. This finding will lead to a forecast of the plant process execution in a cost-effective manner and management of effluent efficiency. The final stage of the thesis was achieved by optimizing the steady state of ASM1.

The regulation of oat coleoptile phosphoenolpyruvate carboxylase and malic enzyme by Hâ ½ and metabolites : kinetic evidence for and against a cytosolic pH-stat

Phosphoenolpyruvate carboxylase (PEPC) and malic enzyme activities in soluble protein extracts of Avena coleoptiles were investigated to determine whether their kinetics were consistent with a role in cytosol pH regulation. Malic enzyme activity was specific for NADP+ and Mn2+. Maximal labelled product formation from [14C]-substrates required the presence of all coenzymes, cofactors and substrates. Plots of rate versus malate concentration, and linear transformations there- 2 of, indicated typical Michaelis-Menten kinetics at non-saturating malate levels and substrate inhibition at higher malate levels. pH increases between 6.5 and 7.25 increased near-optimal activity, decreased the degree of substrate inhibition and the Kmapp(Mn2+) but did not affect the Vmax or Kmapp(malate). Transformed data of PEPC activity demonstrated non-linear plots indicative of non-Michaelian kinetics. pH increases between 7.0 and 7.6 increased the Vmax and decreased the Km app (Mg2+) but did not affect the Kmapp(PEP). Various carboxylic acids and phosphorylated sugars inhibited PEPC and malic enzyme activities, and these effects decreased with pH increases. Metabolite inhibited malic enzyme activity was non-competitive and resulted mainly from Mn2+ chelation. In contrast, metabolite inhibited PEPC activity was unique for each compound tested, being variously dependent on the PEP concentration and the pH employed. These results indicate that fluctuations in pH and metabolite levels affect PEPC and malic enzyme activities similarly and that 3 the in vitro properties of PEPC are consistent with its proposed role in a pH-stat, whereas the in vitro properties of the malic enzyme cannot be interpreted in terms of a role in pH regulation.