Claudio A. Gelmi

Modelling Gibberella fujikuroi growth and GA3 production in solid-state fermentation

A simple differential equation model was developed to represent the growth and production of a secondary metabolite in solid-state cultivation (SSC) under conditions of limited nitrogen. The model was used to interpret data obtained from SSCs of the fungus Gibberella fujikuroi, under different temperatures (25 and 31 °C) and water activity conditions (0.985, 0.992, 0.999). The model was calibrated in two steps. An innovative procedure to achieve good initial guesses for key parameters, such as maintenance coefficients and death rates was first applied. These initial guesses were then used in a non-linear optimisation routine to get a minimum least squares fit for the model. The mathematical model was able to reproduce the measured variables: biomass, urea, starch, CO2 ,O 2 and GA3 satisfactorily. Moreover, the model indicated that the fungus does not assimilate the nitrogen source, urea, directly. The model will be useful in developing optimal feeding policies and on-line biomass estimators.

Solid substrate cultivation of Gibberella fujikuroi on an inert support

Growth of Gibberella fujikuroi on Amberlite, an inert support, and gibberellic acid (GA3) production was studied in glass columns under different conditions of temperature and water activity (aw). Maximum biomass concentration and GA3 production were respectively 40 (mg:g inert support) and 0.73 (mg:g inert support). While high specific growth rates were obtained, low initial nitrogen resulted in low biomass concentrations. Maximum GA3 (31°C, aw 0.985) was not produced by the maximum concentration of biomass (25°C, aw0.992). Peaks in the rate curves of either outlet gas, CO2 or O2, occurred on exhaustion of urea indicating, for future works, just when to feed the culture additional nitrogen.

Does Taking a MOOC as a Complement for Remedial Courses Have an Effect on My Learning Outcomes? A Pilot Study on Calculus

This paper presents the results of a pilot study about students’ adoption and learning outcomes of 4 MOOCs proposed as a complementary resource for traditional remedial courses on calculus. While the MOOCs were not mandatory, traditional remedial courses were required for those freshmen failing a diagnostic exam. The effects on 589 freshmen students were investigated. The data analysis shows that up to 16 % of the students were active in the MOOCs under study, mostly during the days before taking the diagnostic exam that preceded the traditional face-to-face remedial courses. Trace data about learner actions within the platform were collected as well as the students’ scores. According to a statistical comparison of the students’ exam scores and their interaction behavior with the MOOCs, we observe that active students had more chances of passing the diagnostic exam and skipping the required remedial courses. However, we found no significant differences on the remedial course exam scores between the students that were active in the MOOCs and those that were not. These findings suggest that MOOCs are a good solution to strengthening skills and reviewing concepts, but that more guidance is needed when used as a complement to traditional f2f courses.

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.

An exploratory study of peat and sawdust as enhancers in the (bio)degradation of n-dodecane

Current practice for dealing with oil spills involves the use of adsorbent materials to contain the pollution prior to bioremediation of the contaminated soil and adsorbent. This work presents a study of the effects of bioavailable carbon sources in the adsorbents peat and sawdust as organic nutrients for microorganisms specialized in degrading n-dodecane in soil and sawdust contaminated with hydrocarbon mixtures. An experimental bioremediation system was developed using n-dodecane, biomass adapted to n-dodecane, inorganic nutrients and the two adsorbents (sterilized). Bioreactors containing peat enhanced cell growth the most and also evolved more CO2. An advantage of peat is that its soluble carbon sources can sustain higher cell densities compared to sawdust, and this may prove decisive when cultivating endogenous microorganisms for the aerobic bioremediation of soils contaminated with hydrocarbons. However, at the end of the 68-day experiment slightly higher n-dodecane removal was identified in the system containing sawdust-n-dodecane (99.6%) than in that with peat-n-dodecane (98.5%), evidencing the higher hydrocarbon retention capacity of peat. Based on this study, the use of sawdust instead of peat is recommended when an adapted inoculum is available for aerobic bioremediation of organic contaminants, whereas the use of peat is advisable to boost cell densities in order to improve the probability of sustaining a viable biomass in unfavorable conditions.

Operation Strategies to Minimize Methanol Recovery in Batch Distillation of Hydroalcoholic Mixtures

Abstract This work focuses on applying dynamic simulation to explore operating conditions of a packed batch distillation column for reducing the relative concentration of methanol in the distillate. Based on trial-and-error, we studied different cooling rate trajectories of the partial condenser, which has a direct impact on the reflux and distillate rates. Simulations showed that higher cooling rates and smaller cut times achieved lower relative concentrations of methanol in the distillate. Our study suggests that the best operating strategy cannot reduce the relative methanol concentration in the distillate more than 25% compared with the relative methanol concentration in the wine.

Design and implementation of an alternative admission program to engineering: Talent and Inclusion

ABSTRACT In 2011, the School of Engineering at Pontificia Universidad Católica de Chile launched an alternative admission program to attract talented high-school students from low-income families who would not be accepted by the conventional admission process but have otherwise shown outstanding academic results at the high-school level. Applicants came from socioeconomically disadvantaged backgrounds, and were admitted through a formal process that looked for personal skills beyond standardized test results. Students accepted through the alternative admission program showed promising academic gains after their first year of college, a result that has repeated across cohorts. Proper academic and psychological support was needed to level out the disparities in students’ prior knowledge of science and math fundamentals. Future work is required to explore the principal factors affecting academic performance of underrepresented students who are not part of the schools’ dominant culture.