Contribution of exopeptidases to formation of nonprotein nitrogen during ensiling of alfalfa

The experiment was conducted to investigate the exopeptidase classes in alfalfa (Medicago sativa L.) leaves, and to determine their contribution to the formation of nonprotein nitrogen (NPN) components during ensiling. Six classes of inhibitors that included bestatin (aminopeptidase inhibitor), potato carboxypeptidase inhibitor (PCI, carboxypeptidase inhibitor), 1,10-phenanthroline (dipeptidase inhibitor), diprotin A (dipeptidyl-peptidase inhibitor), butabindide (tripeptidyl-peptidase inhibitor), and dipeptide Phe-Arg (peptidyl-dipeptidase inhibitor) were used. To determine the contribution of each exopeptidase to the formation of NPN products, aqueous extracts of fresh alfalfa were fermented to imitate the proteolytic process of ensiled alfalfa and to ensure that each class of exopeptidase inhibitor would have immediate contact with the proteases in the alfalfa extract. Five classes of exopeptidases; namely, aminopeptidase, carboxypeptidase, dipeptidase, dipeptidyl-peptidase, and tripeptidyl-peptidase, were shown to be present in alfalfa leaves, each playing a different role in alfalfa protein degradation. Aminopeptidase, carboxypeptidase, and dipeptidase were the main exopeptidases contributing to the formation of NH3-N. Among the 5 exopeptidases, tripeptidyl-peptidase appeared to be the principal exopeptidase in hydrolyzing forage protein into peptides, whereas carboxypeptidase and dipeptidase appeared to be more important in contributing to the formation of amino acid-N. Dipeptidyl-peptidase and tripeptidyl-peptidase did not play a role in the formation of NH3-N or amino acid-N. Dipeptidase, carboxypeptidase, and tripeptidyl-peptidase were the principal exopeptidases for hydrolyzing forage protein into NPN during ensilage, and treatment with a mixture of the 5 inhibitors reduced the total NPN concentration in the fermented alfalfa extract to about 45% of that in the control after 21 d of fermentation

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Probabilistic Motion Planning for Multi-Robot Systems

Planning safe motions for multi-robot systems is crucial for deploying them in real-world applications such as target tracking, environmental monitoring, and multi-view cinematography. Traditional approaches mainly solve the multi-robot motion planning problem in a deterministic manner, where the robot states and system models are perfectly known. Practically, however, many sources of uncertainty exist in real-world environments, such as noisy sensor measurements, motion disturbances, and uncertain behaviors of other decision-making agents. Reasoning about these uncertainties is of utmost importance for robust and safe navigation of multi-robot systems. To this end, this thesis aims to develop probabilistic methods for multi-robot motion planning under uncertainty. The first main contribution of this thesis is a Chance-Constrained Nonlinear Model Predictive Control (CCNMPC) method for probabilistic multi-robot motion planning. Taking into account uncertainties in robot localization, sensing, and motion disturbances, the method explicitly considers the collision probability between each robot and obstacle and formulates a model predictive control problem with chance constraints. A tight upper bound of the collision probability is developed which makes the CCNMPC formulation tractable and solvable in real time. In addition, the CCNMPC is incorporated into multi-robot motion planning using three coordination strategies: a) centralized sequential planning, b) distributed planning in which robots communicate their future planned trajectories, and c) decentralized planning in which robots predict other robots' trajectories using the constant velocity model (CVM). Performances of the three strategies are analyzed and compared. The CCNMPC method requires robots to know the future trajectories of other robots, either via communication or motion prediction using CVM. However, communication is not always available, and the CVM based motion prediction can lead to collisions among robots, especially in crowded environments. To achieve decentralized and communication-free multi-robot collision avoidance under uncertainty, this thesis then presents a method that relies on the introduced Buffered Uncertainty-Aware Voronoi Cell (B-UAVC). The B-UAVC defines a local safe region for each robot among other robots and obstacles, such that the collision probability between robots and obstacles is below a specified threshold if each robot's motion is constrained to be within its corresponding B-UAVC. An approach to constructing the B-UAVC is proposed, which leverages the techniques of computing a separating hyperplane between two Gaussian distributions and adding buffers for probabilistic collision avoidance. Based on B-UAVC, a set of reactive controllers are designed for single-integrator, double-integrator, and differential-drive robots, respectively; and a receding horizon planner is proposed for general nonlinear dynamical systems. Instead of directly generating a control action for each robot to move towards its waypoint goal as in the CCNMPC and B-UAVC methods, this thesis further presents a method that can compute a safe control input by minimally modifying a given nominal controller, which may come from a high-level task-oriented planner. The method is decentralized and relies on the Chance-Constrained Safety Barrier Certificates (CC-SBC), which defines a probabilistic safe control space for each robot in a multi-robot system considering robot localization and sensing uncertainties. The CC-SBC chance constraints are reformulated into a set of deterministic quadratic constraints, based on which a quadratically constrained quadratic program (QCQP) can be formulated. By solving the QCQP, the robot can obtain a safe control action thanks to that the CC-SBC guarantees forward invariance of the robot's safety set in a probabilistic manner. Hence, the CC-SBC method can be used as a probabilistic safety filter for multi-robot systems. While both the B-UAVC and CC-SBC methods are decentralized and communication-free, they typically lead to more conservative robot motions than the CCNMPC method with robots communicating their planner trajectories. The CCNMPC method can also be communication-free by letting each robot predict the other robots' trajectories using the constant velocity model, but it is unsafe in crowded environments. To address the issue, this thesis finally presents a novel trajectory prediction model based on Recurrent Neural Networks (RNN) that can learn multi-robot motion behaviors from demonstrated trajectories generated using a centralized motion planner. By incorporating the learned RNN-based trajectory prediction model within the MPC framework, efficient and communication-free multi-robot motion planning is achieved. The motion planning methods developed in the thesis have been extensively evaluated and validated in simulations and real-world experiments with a team of quadrotors, showing safe navigation of robots under uncertainty.Learning & Autonomous Contro

Reinforcement Learning in Railway Timetable Rescheduling

Real-time railway traffic management is important for the daily operations of railway systems. It predicts and resolves operational conflicts caused by events like excessive passenger boardings/alightings. Traditional optimization methods for this problem are restricted by the size of the problem instances. Therefore, this paper proposes a reinforcement learning-based timetable rescheduling method. Our method learns how to reschedule a timetable off-line and then can be applied online to make an optimal dispatching decision immediately by sensing the current state of the railway environment. Experiments show that the rescheduling solution obtained by the proposed reinforcement learning method is affected by the state representation of the railway environment. The proposed method was tested to a part of the Dutch railways considering scenarios with single initial train delays and multiple initial train delays. In both cases, our method found high-quality rescheduling solutions within limited training episodes.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and PlanningRailway Engineerin

The use of solubility parameters and free energy theory for phase behaviour of polymer-modified bitumen: a review

Advances related to the use of solubility parameters and free energy theory for the phase behaviour study of polymer-modified bitumen (PMB) are reviewed in this paper. The origin and effects of PMB phase behaviour are criticised with a focus on PMB storage stability, morphology and swelling ratio. An overview of the solubility approach for studying PMB is given regarding the historical and future developments. Free energy expressions for PMB systems are analysed, including the free energy of mixing, elastic free energy and gradient energy. The kinetic aspects are discussed with respect to the diffusion and flow processes. It is indicated that the solubility bodies in the three-dimensional Hansen space and their degree of intersection can be useful for analysing the PMB thermodynamic equilibrium and thus storage stability. But they give no indication by themselves on the PMB morphology. With solubility parameters linked to the PMB free energy, however, an integrated thermodynamic approach can assist in understanding both PMB storage stability and morphology comprehensively. Due to the chemical complexity of bitumen and certain modifiers, the solubility body centres and radiuses should be both considered for a proper expression of the polymer-bitumen interaction in PMB. A hypothetical dilution process can simplify this process, but with limitations. The introduction of elastic free energy may lead to a new and more realistic expression of free energy for PMB system. With this overview, it is expected that a preliminary foundation is established towards a comprehensive and realistic thermodynamic framework for interpreting and predicting PMB phase behaviour.Pavement Engineerin

B-UAVC: Buffered Uncertainty-Aware Voronoi Cells for probabilistic multi-robot collision avoidance

This paper presents B-UAVC, a distributed collision avoidance method for multi-robot systems that accounts for uncertainties in robot localization. In particular, Buffered Uncertainty-Aware Voronoi Cells (B-UAVC) are employed to compute regions where the robots can safely navigate. By computing a set of chance constraints, which guarantee that the robot remains within its B-UAVC, the method can be applied to non-holonomic robots. A local trajectory for each robot is then computed by introducing these chance constraints in a receding horizon model predictive controller. The method guarantees, under the assumption of normally distributed position uncertainty, that the collision probability between the robots remains below a specified threshold. We evaluate the proposed method with a team of quadrotors in simulations and in real experiments.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro