Aline Thaís Bruni

Abstract: A set of 25 quinone compounds with anti-trypanocidal activity was studied by using the density functional theory (DFT) method in order to calculate atomic and molecular properties to be correlated with the biological activity The chemometric methods principal component analysis (PCA), hierarchical cluster analysis (HCA), stepwise discriminant analysis (SDA), Kth nearest neighbor (KNN) and soft independent modeling of class analogy (SIMCA) were used to obtain possible relationships between the calculated descriptors and the biological activity studied and to predict the anti-trypanocidal activity of new quinone compounds from a prediction set Four descriptors were responsible for the separation between the active and inactive compounds: T5 (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO) These descriptors give information on the kind of interaction that occurs between the compounds and the biological receptor The prediction study was done with a set of three new compounds by using the PCA, HCA, SDA, KNN and SIMCA methods and two of them were predicted as active against the Trypanosoma cruzi

Abstract: Multivariate analysis was used to identify the variables affecting the performance of pilot-scale activated sludge (AS) reactors treating old leachate from a landfill and from domestic wastewater. Raw leachate was pre-treated using air stripping to partially remove the total ammoniacal nitrogen (TAN). The control AS reactor (AS-0%) was loaded only with domestic wastewater, whereas the other reactor was loaded with mixtures containing leachate at volumetric ratios of 2 and 5%. The best removal efficiencies were obtained for a ratio of 2%, as follows: 70 ± 4% for total suspended solids (TSS), 70 ± 3% for soluble chemical oxygen demand (SCOD), 70 ± 4% for dissolved organic carbon (DOC), and 51 ± 9% for the leachate slowly biodegradable organic matter (SBOM). Fourier transform infrared (FTIR) spectroscopic analysis confirmed that most of the SBOM was removed by partial biodegradation rather than dilution or adsorption of organics in the sludge. Nitrification was approximately 80% in the AS-0% and AS-2% reactors. No significant accumulation of heavy metals was observed for any of the tested volumetric ratios. Principal component analysis (PCA) and partial least squares (PLS) indicated that the data dimension could be reduced and that TAN, SCOD, DOC and nitrification efficiency were the main variables that affected the performance of the AS reactors.

Abstract: Departamento de Fisica Instituto de Biociencias, Letras e Ciencias Exatas Universidade Estadual Paulista, Sao Jose do Rio Preto, Sao Paulo, 15054-000

Abstract: The quantitative structure-activity relationship of a set of 19 flavonoid compounds presenting antioxidant activity was studied by means of PLS (Partial Least Squares) regression. The optimization of the structures and calculation of electronic properties were done by using the semiempirical method AM1. A reliable model (r 2=0.806 and q 2=0.730) was obtained and from this model it was possible to consider some aspects of the structure of the flavonoid compounds studied that are related with their free radical scavenging ability. The quality of the PLS model obtained in this work indicates that it can be used in order to design new flavonoid compounds that present ability to scavenge free radicals.

Abstract: The performance of an anaerobic baffled reactor (ABR) was evaluated in the treatment of cassava wastewater, a pollutant residue. An ABR divided in four equal volume compartments (total volume 4L) and operated at 35°C was used in cassava wastewater treatment. Feed tank chemical oxygen demand (COD) was varied from 2000 to 7000 mg L-1 and it was evaluated the most appropriated hydraulic retention time (HRT) for the best performance on COD removal. The ABR was evaluated by analysis of COD (colorimetric method), pH, turbidity, total and volatile solids, alkalinity and acidity. Principal component analysis (PCA) was carried to better understand data obtained. The system showed buffering ability as acidity decreased along compartments while alkalinity and pH values were increased. There was particulate material retention and COD removal varied from 83 to 92% for HRT of 3.5 days.

Abstract: Background and purpose: Accumulated evidence suggests that oxidative stress is involved in amyloid β (Aβ)-induced cognitive dysfunction. Silibinin (silybin), a flavonoid derived from the herb milk thistle (Silybum marianum), has been shown to have antioxidative properties; however, it remains unclear whether silibinin improves Aβ-induced neurotoxicity. In the present study, we examined the effect of silibinin on the memory impairment and accumulation of oxidative stress induced by Aβ25–35 in mice.

Abstract: The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uni-exponential {\em vs.} multi-exponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.

Abstract: The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.

Abstract: Palm oil mill effluent (POME) is generated from the sterilization, condensation and hydrocycloning of palm oil in mills. If the effluent is discharged into the aquatic and terrestrial ecosystem without treatment, it could lead to high biological oxygen demand (BOD), chemical oxygen demand (COD) and acidic pH of the receiving waters. Biogas consisting mostly of methane, carbon dioxide, and to a lesser hydrogen has been produced through anaerobic treatment of this toxic effluent. The process of biogas production involves microbial synthesis involving hydrolysis, acidogenesis, acetogenesis and methanogenesis. Biogas is formed during anaerobic degradation of POME by indigenous microbial communities. This review updates the current state of art of biogas production through anaerobic digestion of POME using different configurations of reactors such as fluidized bed reactor, anaerobic filtration, up-flow anaerobic sludge blanket (UASB) reactor, anaerobic contact digestion, up-flow anaerobic sludge fixed-film (UASFF) reactor, modified anaerobic baffled bioreactor (MABB), anaerobic baffled bioreactor (ABR), continuous stirred tank reactor (CSTR), expanded granular sludge bed (EGSB) reactor, Ultrasonicated membrane anaerobic system (UMAS), Ultrasonic-assisted Membrane Anaerobic System (UAMAS), membrane anaerobic system (MAS)and upflow anaerobic sludge blanket reactor (UASBR). The factors that influences biogas yield during treatment include pH, temperature (environmental factors), organic loading rate (OLR), hydraulic retention time (HRT), mixing rate, pressure, equilibrium, nutrient and microbial activities (Internal factors). Based on this study, UAMAS is the best configuration for methane production from POME during anaerobic treatment. Biogas from POME could contribute to energy sources of oil palm producing nations, while preventing the attendant environmental impacts associated with its disposal.

Abstract: Despite the high prevalence of neuropsychiatric disorders, their aetiology and molecular mechanisms remain poorly understood. The zebrafish (Danio rerio) is increasingly utilized as a powerful animal model in neuropharmacology research and in vivo drug screening. Collectively, this makes zebrafish a useful tool for drug discovery and the identification of disordered molecular pathways. Here, we discuss zebrafish models of selected human neuropsychiatric disorders and drug-induced phenotypes. As well as covering a broad range of brain disorders (from anxiety and psychoses to neurodegeneration), we also summarize recent developments in zebrafish genetics and small molecule screening, which markedly enhance the disease modelling and the discovery of novel drug targets.