Integrated bioprocess approach for the production of xylooligosaccharides

The demand of prebiotic ingredients has been growing over the years as consumers pay more attention to their health. Xylooligosaccharides (XOS) are considered emergent and competitively priced prebiotics, presenting high potential as food ingredients. As a result, the industry is focused on developing new approaches to improve their production efficiency to meet the increasing demand while reducing costs. Hence, the main purpose of this work was to develop an integrated bioprocess, based on one-step fermentation, for the production of prebiotic XOS, towards the simplification and cost reduction of the process. The one-step fermentation of 13 agro-residues was done using two Trichoderma species. The most promising results were found for T. reesei using brewers spent grain (BSG) as substrate. BSG is an inexpensive and abundant agroindustrial residue that was proven interesting for the production of arabino-xylooligosaccharides (AXOS). In order to reduce the production time obtained with T. reesei (3 d), the Bacillus subtilis 3610 wild type (wt) was successfully used to produce AXOS through direct fermentation of BSG, reducing the production time to 12 h. Genetic engineering was used to further optimize the microorganism performance, by cloning the T. reesei xylanase gene coupled with a secretion tag into the B. subtilis chromosome (B. subtilis 3610 clone 2). This strategy led to a yield increase of 33 % comparing to the wt, and 29 % comparing to the T. reesei. B. subtilis 3610 clone 2 was also selected for downscale production of XOS by direct fermentation of commercial beechwood xylan. The maximum production yield, 306 ± 4 mg/g (XOS/xylan), was achieved after 8 h of fermentation operating under one-time impulse fed-batch regimen. In vitro studies using human fecal inocula were performed to evaluate and compare the potential prebiotic effect of commercial lactulose and the XOS herein produced. The significant increase in the production of short chain fatty acids and CO2, added to the reduction of pH and ammonia concentration suggest that the XOS hold potential functional properties for human health. The results gathered provide important insights for the development of new integrated strategies for XOS production from agro-residues.info:eu-repo/semantics/publishedVersio

An Uncertainty-Aware Minimal Intervention Control Strategy Learned from Demonstrations

Motivated by the desire to have robots physically present in human environments, in recent years we have witnessed an emergence of different approaches for learning active compliance. Some of the most compelling solutions exploit a minimal intervention control principle, correcting deviations from a goal only when necessary, and among those who follow this concept, several probabilistic techniques have stood out from the rest. However, these approaches are prone to requiring several task demonstrations for proper gain estimation and to generating unpredictable robot motions in the face of uncertainty. Here we present a Programming by Demonstration approach for uncertainty-aware impedance regulation, aimed at making the robot compliant - and safe to interact with - when the uncertainty about its predicted actions is high. Moreover, we propose a data-efficient strategy, based on the energy observed during demonstrations, to achieve minimal intervention control, when the uncertainty is low. The approach is validated in an experimental scenario, where a human collaboratively moves an object with a 7-DoF torque-controlled robot

Hybrid Probabilistic Trajectory Optimization Using Null-Space Exploration

In the context of learning from demonstration, human examples are usually imitated in either Cartesian or joint space. However, this treatment might result in undesired movement trajectories in either space. This is particularly important for motion skills such as striking, which typically imposes motion constraints in both spaces. In order to address this issue, we consider a probabilistic formulation of dynamic movement primitives, and apply it to adapt trajectories in Cartesian and joint spaces simultaneously. The probabilistic treatment allows the robot to capture the variability of multiple demonstrations and facilitates the mixture of trajectory constraints from both spaces. In addition to this proposed hybrid space learning, the robot often needs to consider additional constraints such as motion smoothness and joint limits. On the basis of Jacobian-based inverse kinematics, we propose to exploit robot null-space so as to unify trajectory constraints from Cartesian and joint spaces while satisfying additional constraints. Evaluations of hand-shaking and striking tasks carried out with a humanoid robot demonstrate the applicability of our approach

Kernelized movement primitives

Imitation learning has been studied widely as a convenient way to transfer human skills to robots. This learning approach is aimed at extracting relevant motion patterns from human demonstrations and subsequently applying these patterns to different situations. Despite the many advancements that have been achieved, solutions for coping with unpredicted situations (e.g., obstacles and external perturbations) and high-dimensional inputs are still largely absent. In this paper, we propose a novel kernelized movement primitive (KMP), which allows the robot to adapt the learned motor skills and fulfill a variety of additional constraints arising over the course of a task. Specifically, KMP is capable of learning trajectories associated with high-dimensional inputs owing to the kernel treatment, which in turn renders a model with fewer open parameters in contrast to methods that rely on basis functions. Moreover, we extend our approach by exploiting local trajectory representations in different coordinate systems that describe the task at hand, endowing KMP with reliable extrapolation capabilities in broader domains. We apply KMP to the learning of time-driven trajectories as a special case, where a compact parametric representation describing a trajectory and its first-order derivative is utilized. In order to verify the effectiveness of our method, several examples of trajectory modulations and extrapolations associated with time inputs, as well as trajectory adaptations with high-dimensional inputs are provided

Generalized Task-Parameterized Skill Learning

Programming by demonstration has recently gained much attention due to its user-friendly and natural way to transfer human skills to robots. In order to facilitate the learning of multiple demonstrations and meanwhile generalize to new situations, a task-parameterized Gaussian mixture model (TP-GMM) has been recently developed. This model has achieved reliable performance in areas such as human-robot collaboration and dual-arm manipulation. However, the crucial task frames and associated parameters in this learning framework are often set by the human teacher, which renders three problems that have not been addressed yet: (i) task frames are treated equally, without considering their individual importance, (ii) task parameters are defined without taking into account additional task constraints, such as robot joint limits and motion smoothness, and (iii) a fixed number of task frames are pre-defined regardless of whether some of them may be redundant or even irrelevant for the task at hand. In this paper, we generalize the task-parameterized learning by addressing the aforementioned problems. Moreover, we provide a novel learning perspective which allows the robot to refine and adapt previously learned skills in a low dimensional space. Several examples are studied in both simulated and real robotic systems, showing the applicability of our approach

Non-parametric Imitation Learning of Robot Motor Skills

Unstructured environments impose several challenges when robots are required to perform different tasks and adapt to unseen situations. In this context, a relevant problem arises: how can robots learn to perform various tasks and adapt to different conditions? A potential solution is to endow robots with learning capabilities. In this line, imitation learning emerges as an intuitive way to teach robots different motor skills. This learning approach typically mimics human demonstrations by extracting invariant motion patterns and subsequently applies these patterns to new situations. In this paper, we propose a novel kernel treatment of imitation learning, which endows the robot with imitative and adaptive capabilities. In particular, due to the kernel treatment, the proposed approach is capable of learning human skills associated with high-dimensional inputs. Furthermore, we study a new concept of correlation-adaptive imitation learning, which allows for the adaptation of correlations exhibited in high-dimensional demonstrated skills. Several toy examples and a collaborative task with a real robot are provided to verify the effectiveness of our approach

Screening of fungal sources of -galactosidase with potential for the synthesis of prebiotics

Book of Abstracts of CEB Annual Meeting 2017[Excerpt] β-Galactosidases (EC 3.2.1.23), also known as lactases, are a family of enzymes able to catalyse two different types of reactions, namely hydrolysis and transgalactosylation. The hydrolytic activity is commonly applied in the food industries to reduce the lactose content of dairy products, preventing lactose crystallization problems and increasing sweetness, flavour and solubility. On the other hand, transgalactosylation reactions have been explored in the synthesis of lactose-based prebiotics, such as galacto-oligosaccharides (GOS), lactosucrose [1] and lactulose [2], with potential application in the pharmaceutical and food industry. These prebiotics are enzymatically produced through the hydrolysis of lactose and further transfer of a galactosyl residue to a suitable acceptor, i.e. fructose for the disaccharide lactulose; sucrose for the trisaccharide lactosucrose; and lactose for GOS. The sources of βgalactosidase are extensively distributed in nature, namely in microorganisms, plants and animal organs. [...]info:eu-repo/semantics/publishedVersio

Avaliação de metodologia para análise de íons por cromatografia iônica em água de nascentes.

Editores técnicos: Marcílio José Thomazini, Elenice Fritzsons, Patrícia Raquel Silva, Guilherme Schnell e Schuhli, Denise Jeton Cardoso, Luziane Franciscon. EVINCI. Resumos

Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints

When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task space, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned probabilistically from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating new torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7- DoF torque-controlled manipulators, with tasks that require the consideration of different controllers to be properly executed

Produção de lipase a partir de Candida rugosa NRRL Y-95 utilizando meio de cultura contendo resíduos agroindustriais

As lipases (E.C. 3.1.1.3) são um grupo de enzimas capazes de catalisar a hidrólise da ligação éster de triacilgliceróis, gerando ácidos graxos livres e glicerol. As lipases microbianas são muito utilizadas nas aplicações industriais nas áreas de alimentos, síntese orgânica e farmacêutica. Neste trabalho, visando à produção de lipase a partir da levedura Candida rugosa foram utilizados meios de cultura alternativos compostos por melaço, milhocina e águas russas. As fermentações foram conduzidas em agitador rotatório a 30 °C e 170 rpm. Testaram-se quatro meios contendo diferentes combinações dos resíduos acima mencionados. O meio contendo melaço 10 g/L, milhocina 4 g/L e águas russas 1,0 %(v/v) foi o que propiciou a produção de enzima com maior atividade intracelular 269 ± 10 U/L frente ao substrato pNFL (p-nitrofenil laurato). Esses resultados demonstram que o referido meio alternativo contendo resíduos agroindustriais é adequado para a produção de lipase