Environmental variability in aquatic ecosystems: avenues for future multifactorial experiments

The relevance of considering environmental variability for understanding and predicting biological responses to environmental changes has resulted in a recent surge in variability-focused ecological research. However, integration of findings that emerge across studies and identification of remaining knowledge gaps in aquatic ecosystems remain critical. Here, we address these aspects by: (1) summarizing relevant terms of variability research including the components (characteristics) of variability and key interactions when considering multiple environmental factors; (2) identifying conceptual frameworks for understanding the consequences of environmental variability in single and multi-factorial scenarios; (3) highlighting challenges for bridging theoretical and experimental studies involving transitioning from simple to more complex scenarios; (4) proposing improved approaches to overcome current mismatches between theoretical predictions and experimental observations; and (5) providing a guide for designing integrated experiments across multiple scales, degrees of control, and complexity in light of their specific strengths and limitations

Nicotiana benthamiana as a Production Platform for Artemisinin Precursors

Background Production of pharmaceuticals in plants provides an alternative for chemical synthesis, fermentation or natural sources. Nicotiana benthamiana is deployed at commercial scale for production of therapeutic proteins. Here the potential of this plant is explored for rapid production of precursors of artemisinin, a sesquiterpenoid compound that is used for malaria treatment. Methodology/Principal Findings Biosynthetic genes leading to artemisinic acid, a precursor of artemisinin, were combined and expressed in N. benthamiana by agro-infiltration. The first committed precursor of artemisinin, amorpha-4,11-diene, was produced upon infiltration of a construct containing amorpha-4,11-diene synthase, accompanied by 3-hydroxy-3-methylglutaryl-CoA reductase and farnesyl diphosphate synthase. Amorpha-4,11-diene was detected both in extracts and in the headspace of the N. benthamiana leaves. When the amorphadiene oxidase CYP71AV1 was co-infiltrated with the amorphadiene-synthesizing construct, the amorpha-4,11-diene levels strongly decreased, suggesting it was oxidized. Surprisingly, no anticipated oxidation products, such as artemisinic acid, were detected upon GC-MS analysis. However, analysis of leaf extracts with a non-targeted metabolomics approach, using LC-QTOF-MS, revealed the presence of another compound, which was identified as artemisinic acid-12-ß-diglucoside. This compound accumulated to 39.5 mg.kg-1 fwt. Apparently the product of the heterologous pathway that was introduced, artemisinic acid, is further metabolized efficiently by glycosyl transferases that are endogenous to N. benthamiana. Conclusion/Significance This work shows that agroinfiltration of N. bentamiana can be used as a model to study the production of sesquiterpenoid pharmaceutical compounds. The interaction between the ectopically introduced pathway and the endogenous metabolism of the plant is discussed

Syntactic learning by mere exposure - An ERP study in adult learners

<p>Abstract</p> <p>Background</p> <p>Artificial language studies have revealed the remarkable ability of humans to extract syntactic structures from a continuous sound stream by mere exposure. However, it remains unclear whether the processes acquired in such tasks are comparable to those applied during normal language processing. The present study compares the ERPs to auditory processing of simple Italian sentences in native and non-native speakers after brief exposure to Italian sentences of a similar structure. The sentences contained a non-adjacent dependency between an auxiliary and the morphologically marked suffix of the verb. Participants were presented four alternating learning and testing phases. During learning phases only correct sentences were presented while during testing phases 50 percent of the sentences contained a grammatical violation.</p> <p>Results</p> <p>The non-native speakers successfully learned the dependency and displayed an N400-like negativity and a subsequent anteriorily distributed positivity in response to rule violations. The native Italian group showed an N400 followed by a P600 effect.</p> <p>Conclusion</p> <p>The presence of the P600 suggests that native speakers applied a grammatical rule. In contrast, non-native speakers appeared to use a lexical form-based processing strategy. Thus, the processing mechanisms acquired in the language learning task were only partly comparable to those applied by competent native speakers.</p

Interactive Language Learning by Robots: The Transition from Babbling to Word Forms

The advent of humanoid robots has enabled a new approach to investigating the acquisition of language, and we report on the development of robots able to acquire rudimentary linguistic skills. Our work focuses on early stages analogous to some characteristics of a human child of about 6 to 14 months, the transition from babbling to first word forms. We investigate one mechanism among many that may contribute to this process, a key factor being the sensitivity of learners to the statistical distribution of linguistic elements. As well as being necessary for learning word meanings, the acquisition of anchor word forms facilitates the segmentation of an acoustic stream through other mechanisms. In our experiments some salient one-syllable word forms are learnt by a humanoid robot in real-time interactions with naive participants. Words emerge from random syllabic babble through a learning process based on a dialogue between the robot and the human participant, whose speech is perceived by the robot as a stream of phonemes. Numerous ways of representing the speech as syllabic segments are possible. Furthermore, the pronunciation of many words in spontaneous speech is variable. However, in line with research elsewhere, we observe that salient content words are more likely than function words to have consistent canonical representations; thus their relative frequency increases, as does their influence on the learner. Variable pronunciation may contribute to early word form acquisition. The importance of contingent interaction in real-time between teacher and learner is reflected by a reinforcement process, with variable success. The examination of individual cases may be more informative than group results. Nevertheless, word forms are usually produced by the robot after a few minutes of dialogue, employing a simple, real-time, frequency dependent mechanism. This work shows the potential of human-robot interaction systems in studies of the dynamics of early language acquisition

Effectiveness of a multimodal training programme to improve general practitioners' burnout, job satisfaction and psychological well-being

BACKGROUND: The changes in the models of care for mental disorders towards a community focus and deinstitutionalisation might have risen General practitioners' (GPs) workload, increasing their mental health concerns and the need for solutions. Pragmatic research into improving GPs' work-related health and psychological well-being is limited by focusing mainly on stressors and through not providing systematic attention to the development of positive mental health via interventions that develop psychological resources and capacities. The aim of this study was twofold: a) to determine the effectiveness of an intensive multimodal training programme for GPs designed to improve their management of mental-health patients; and b) to ascertain if the program could be also useful to improve the GPs management of their own burnout, job satisfaction and psychological well-being. METHOD: Eighteen GPs constituted a control group that underwent the routine clinical Mental health support programme for primary care. An experimental group (N = 20) additionally received a Multimodal training programme (MTP) with an Integrated Brief Systemic Therapy (IBST) approach. Through questionnaires and a clinical interview, level of burnout, professional satisfaction, psychopathological state and various indicators of the quality of administrative and healthcare management were analysed at baseline and 10 months after the programme. RESULTS: In relation to government of mental-health patients indicators, on the one hand MTP group showed statistically significant improvements in certain administrative health parameters, but on the other it did not improve opinions and attitudes towards mental illness. Regarding GPs management of their own burnout, job satisfaction and psychological well-being assessments, the MTP presented better scores on global psychopathological state and better evolution of satisfaction at work; psychopharmacology use dropped in both groups; in contrast, the MTP did not improve burnout levels. CONCLUSIONS: Findings of this preliminary study are promising for the MTP (with an IBST approach) practice in primary care. More research evidence is required from larger samples and randomized controlled trials to support both the hypothetical adoption of MTP (with an IBST approach) as a part of a continuing professional-training programme for GPs' management of mental-health patients and its positive effects on work-related health factors

A Literature Review on Train Motion Model Calibration

The dynamics of a moving train are usually described by means of a motion model based on Newton&amp;#x2019;s second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on-board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on-the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.</p

Farnesyl Diphosphate Synthase Assay

Farnesyl diphosphate synthase (FPS) catalyzes the sequential head-to-tail condensation of isopentenyl diphosphate (IPP, C5) with dimethylallyl diphosphate (DMAPP, C5) and geranyl diphosphate (GPP, C10) to produce farnesyl diphosphate (FPP, C15). This short-chain prenyl diphosphate constitutes a key branch-point of the isoprenoid biosynthetic pathway from which a variety of bioactive isoprenoids that are vital for normal plant growth and survival are produced. Here we describe a protocol to obtain highly purified preparations of recombinant FPS and a radiochemical assay method for measuring FPS activity in purified enzyme preparations and plant tissue extracts.This work was supported by grants from the Spanish Ministerio de Ciencia e Innovación (BIO2009-06984, cofinanced by the European Regional Development Funds), the Spanish Consolider-Ingenio 2010 Program (CSD2007-00036, Center for Research in Agricultural Genomics), and the Generalitat de Catalunya (2009SGR0026).Peer reviewe

Real-time train motion parameter estimation using an Unscented Kalman Filter

Train movement dynamics are usually modelled by means of Newton's second law. The resulting dynamic equation can be very precise if the parameters that it depends on are determined accurately. However, these parameters may vary in time and show wide variations, making the calibration task nontrivial and jeopardizing the performance of a broad variety of applications in the railway industry: from timetable planning and railway traffic simulation to Driver Advisory Systems and Automatic Train Operation. In this article, the online train motion model calibration problem is addressed with a special focus on energy-efficient on-board applications. To this end, location and speed measurements are assumed to be available for a train running under normal operation conditions. A well-known real-time parameter estimation algorithm, the Unscented Kalman Filter, is combined with a driving regime calculator and a post-processing module in order to obtain bounds and statistics of parameters such as the maximum applied tractive effort and power, the applied brake rates, the cruise speed and the length of the final coasting and braking. The proposed framework is tested in a case study with real data from trains operating on the Eindhoven-’s-Hertogenbosch corridor in the Netherlands. Results obtained show that UKF is able to track the speed and location measurements and to estimate the parameters that model the running resistance in the dynamic equation. The proposed driving regime and the post-processing modules can determine the current regime accurately and give a deeper insight into the variations of the driving style, respectively.Transport and Plannin

Train motion model calibration: Research agenda and practical recommendations

An accurate train motion model is a key component of a wide spectrum of railway applications, from timetabling algorithms to Automatic Train Operation systems. Therefore, model calibration has become crucial in the railway industry, although this topic has not received the attention and recognition in academia that its practical relevance deserves. Several data-driven techniques have been devised to calibrate train dynamics models, although an overview that describes the current state of the art in the field and highlights the following steps to be researched is still missing in the literature. Thus, this article has four main goals. First, giving a brief insight into the broad variety of techniques used for train motion model calibration, focusing on those techniques that use on-board measurements and are applicable in railway operation. Second, highlighting the main research steps to be tackled, considering the current main challenges in railway research. Third, outlining practical recommendations to practitioners who need to calibrate their algorithms and applications. And fourth, contributing to giving train motion model calibration its due recognition.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 Plannin