Bayesian nonparametric analysis of reversible Markov chains

We introduce a three-parameter random walk with reinforcement, called the $(\theta,\alpha,\beta)$ scheme, which generalizes the linearly edge reinforced random walk to uncountable spaces. The parameter $\beta$ smoothly tunes the $(\theta,\alpha,\beta)$ scheme between this edge reinforced random walk and the classical exchangeable two-parameter Hoppe urn scheme, while the parameters $\alpha$ and $\theta$ modulate how many states are typically visited. Resorting to de Finetti's theorem for Markov chains, we use the $(\theta,\alpha,\beta)$ scheme to define a nonparametric prior for Bayesian analysis of reversible Markov chains. The prior is applied in Bayesian nonparametric inference for species sampling problems with data generated from a reversible Markov chain with an unknown transition kernel. As a real example, we analyze data from molecular dynamics simulations of protein folding.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1102 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Production of bioethanol from multiple waste streams of rice milling

This work describes the feasibility of using rice milling by-products as feedstock for bioethanol. Starch-rich residues (rice bran, broken, unripe and discolored rice) were individually fermented (20% w/v) through Consolidated Bioprocessing by two industrial engineered yeast secreting fungal amylases. Rice husk (20% w/v), mainly composed by lignocellulose, was pre-treated at 55 degrees C with alkaline peroxide, saccharified through optimized dosages of commercial enzymes (Cellic (R) CTec2) and fermented by the recombinant strains. Finally, a blend of all the rice by-products, formulated as a mixture (20% w/v) according to their proportions at milling plants, were co-processed to ethanol by optimized pre-treatment, saccharification and fermentation by amylolytic strains. Fermenting efficiency for each by-product was high (above 88% of the theoretical) and further confirmed on the blend of residues (nearly 52 g/L ethanol). These results demonstrated for the first time that the co-conversion of multiple waste streams is a promising option for second generation ethanol production

Utilisation of wheat bran as a substrate for bioethanol production using recombinant cellulases and amylolytic yeast

Wheat bran, generated from the milling of wheat, represents a promising feedstock for the production of bioethanol. This substrate consists of three main components: starch, hemicellulose and cellulose. The optimal conditions for wheat bran hydrolysis have been determined using a recombinant cellulase cocktail (RCC), which contains two cellobiohydrolases, an endoglucanase and a beta-glucosidase. The 10% (w/v, expressed in terms of dry matter) substrate loading yielded the most glucose, while the 2% loading gave the best hydrolysis efficiency (degree of saccharification) using unmilled wheat bran. The ethanol production of two industrial amylolytic Saccharomyces cerevisiae strains, MEL2[TLG1-SFA1] and M2n [TLG1-SFA1], were compared in a simultaneous saccharification and fermentation (SSF) for 10% wheat bran loading with or without the supplementation of optimised RCC. The recombinant yeasts. cerevisiae MEL2[TLG1-SFA1] and M2n[TLG1-SFA1] completely hydrolysed wheat bran's starch producing similar amounts of ethanol (5.3 +/- 0.14 g/L and 5.0 +/- 0.09 g/L, respectively). Supplementing SSF with RCC resulted in additional ethanol production of about 2.0 g/L. Scanning electron microscopy confirmed the effectiveness of both RCC and engineered amylolytic strains in terms of cellulose and starch depolymerisatio

Innately robust yeast strains isolated from grape marc have a great potential for lignocellulosic ethanol production

Bioethanol from lignocellulose is an attractive alternative to fossil fuels, and Saccharomyces cerevisiae is the most important ethanol producer. However, yeast cells are challenged by various environmental stresses during ethanol production on an industrial scale, and robust strains with a high tolerance to inhibitors, temperature and osmolality are needed for the effective feasibility of lignocellulosic ethanol. To search for such innately more resistant yeast, we selected grape marc as an extreme environment due to limited nutrients, exposure to solar radiation, temperature fluctuations, weak acids and ethanol. Using a temperature of 40 A degrees C as the key selection criterion, we isolated 120 novel S. cerevisiae strains from grape marc and found high ethanol yields (up to 92 % of the theoretical maximum) when inoculated at 40 A degrees C in minimal media with a high sugar concentration. For the first time, this work assessed yeast tolerance to inhibitors at 40 A degrees C, and the newly isolated yeast strains displayed interesting abilities to withstand increasing levels of single inhibitors or cocktails containing a mixture of inhibitory compounds. The newly isolated strains showed significantly higher fermentative abilities and tolerance to inhibitors than the industrial and commercial benchmark S. cerevisiae strains. The strong physiological robustness and fitness of a few of these S. cerevisiae yeast strains support their potential industrial application and encourage further studies in genetic engineering to enhance their ethanol performance in terms of rate and yield through the co-fermentation of all available carbon sources

Bayesian Regularization of the Length of Memory in Reversible Sequences

Summary Variable order Markov chains have been used to model discrete sequential data in a variety of fields. A host of methods exist to estimate the history-dependent lengths of memory which characterize these models and to predict new sequences. In several applications, the data-generating mechanism is known to be reversible, but combining this information with the procedures mentioned is far from trivial. We introduce a Bayesian analysis for reversible dynamics, which takes into account uncertainty in the lengths of memory. The model proposed is applied to the analysis of molecular dynamics simulations and compared with several popular algorithms.SF is supported by the European Research Council through grant StG N-BNP 306406, LT has been supported by the Claudia Adams Barr Program in Innovative Cancer Research and SB received funding from the Stein Fellowship.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/rssb.1214

More for less: Predicting and maximizing genetic variant discovery via Bayesian nonparametrics

While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains non-trivial. Under a fixed budget, then, scientists face a natural trade-off between quantity and quality; they can spend resources to sequence a greater number of genomes (quantity) or spend resources to sequence genomes with increased accuracy (quality). Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in the genome as possible, and thus as many new scientific insights as possible. In this paper, we consider the common setting where scientists have already conducted a pilot study to reveal variants in a genome and are contemplating a follow-up study. We introduce a Bayesian nonparametric methodology to predict the number of new variants in the follow-up study based on the pilot study. When experimental conditions are kept constant between the pilot and follow-up, we demonstrate on real data from the gnomAD project that our prediction is more accurate than three recent proposals, and competitive with a more classic proposal. Unlike existing methods, though, our method allows practitioners to change experimental conditions between the pilot and the follow-up. We demonstrate how this distinction allows our method to be used for (i) more realistic predictions and (ii) optimal allocation of a fixed budget between quality and quantity

Bayesian regularization of the length of memory in reversible sequences

Variable order Markov chains have been used to model discrete sequential data in a variety of fields. A host of methods exist to estimate the history-dependent lengths of memory which characterize these models and to predict new sequences. In several applications, the data-generating mechanism is known to be reversible, but combining this information with the procedures mentioned is far from trivial. We introduce a Bayesian analysis for reversible dynamics, which takes into account uncertainty in the lengths of memory. The model proposed is applied to the analysis of molecular dynamics simulations and compared with several popular algorithms.SF is supported by the European Research Council through grant StG N-BNP 306406, LT has been supported by the Claudia Adams Barr Program in Innovative Cancer Research and SB received funding from the Stein Fellowship.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/rssb.1214

A Nonparametric Bayes Approach to Online Activity Prediction

Accurately predicting the onset of specific activities within defined timeframes holds significant importance in several applied contexts. In particular, accurate prediction of the number of future users that will be exposed to an intervention is an important piece of information for experimenters running online experiments (A/B tests). In this work, we propose a novel approach to predict the number of users that will be active in a given time period, as well as the temporal trajectory needed to attain a desired user participation threshold. We model user activity using a Bayesian nonparametric approach which allows us to capture the underlying heterogeneity in user engagement. We derive closed-form expressions for the number of new users expected in a given period, and a simple Monte Carlo algorithm targeting the posterior distribution of the number of days needed to attain a desired number of users; the latter is important for experimental planning. We illustrate the performance of our approach via several experiments on synthetic and real world data, in which we show that our novel method outperforms existing competitors

Autotrophic production of polyhydroxyalkanoates using acidogenic-derived H2 and CO2 from fruit waste

[Abstract] The environmental concerns regarding fossil plastics call for alternative biopolymers such as polyhydroxyalkanoates (PHAs) whose manufacturing costs are however still too elevated. Autotrophic microbes like Cupriavidus necator, able to convert CO2 and H2 into PHAs, offer an additional strategy. Typically, the preferred source for CO2 and H2 are expensive pure gases or syngas, which has toxic compounds for most PHAs-accumulating strains. In this work, for the first time, H2 and CO2 originating from an acidogenic reactor were converted autotrophically into poly(3-hydroxybutyrate) P(3HB). During the first stage, a mixed microbial community continuously catabolized melon waste into H2 (26.7 %) and CO2 (49.2 %) that were then used in a second bioreactor by C. necator DSM 545 to accumulate 1.7 g/L P(3HB). Additionally, the VFAs (13 gCOD/L) produced during acidogenesis were processed into 2.7 g/L of P(3HB-co-3HV). This is the first proof-of-concept of using acidogenic-derived H2 and CO2 from fruit waste to produce PHAs.This work was funded by Università degli Studi di Padova through BIRD210708/21 and DOR2352129/23 and Xunta de Galicia (Spain) through the Competitive Reference Research Groups grant (ED431C 2021/55 project). The authors thank FRANCESCON OP SOC.AGR. SOC.CONS. a.r.l. for the donation of the melon waste. Additionally, the authors thank Professor Veiga and Professor Kennes’s team for the technical support with the maintenance of bioreactors.Xunta de Galicia; ED431C 2021/55Italia. Università degli Studi di Padova; BIRD210708/21Italia. Università degli Studi di Padova; DOR2352129/2