Morphological and biochemical changes in Phaeodactylum tricornutum triggered by culture media: Implications for industrial exploitation

Phaeodactylum tricornutum is a polymorphic marine diatom, displaying three main morphotypes: fusiform, triradiate and oval. It is of great interest for industrial biotechnology as a natural rich source of valuable eicosapentaenoic acid (EPA) and fucoxanthin. Changing culture conditions such as temperature and salinity has been shown to elicit morphological changes in P. tricornutum. However, limited information is available about the conditions that can be used for controlling cell morphology and growth of a particular cell morphotype with high biomass productivity. While the phenomenon of pleiomorphy is intrinsically interesting, there has not been a systematic study linking this behavior to the ability of P. tricornutum to perform as a platform for industrial biotechnology. In this study, the effects of culture medium and culture age on morphological and biochemical changes in P. tricornutum were investigated. Mann and Myers' medium was identified as eliciting significant morphotype conversion from fusiform to oval in P. tricornutum. Liquid cultures containing >90% oval cells were obtained and well-maintained in this medium under constant shaking condition, allowing high dry biomass concentration (0.73 g L−1) to be achieved. Biochemical composition analyses revealed that higher protein (% dry weight) was obtained from oval cell cultures compared to fusiform cell cultures maintained in f/2 medium over 21 days cultivation. Meanwhile, pigment was markedly accumulated in oval cell cultures whereas lipid and carbohydrate were highly accumulated in fusiform cell cultures. This work offered a novel way to regulate cell morphology of P. tricornutum and provided significant implications for upstream cultivation strategies to optimise manufacture of different classes of product in P. tricornutum

Optimal block designs for experiments on networks

© 2021 The Authors. We propose a method for constructing optimal block designs for experiments on networks. The response model for a given network interference structure extends the linear network effects model to incorporate blocks. The optimality criteria are chosen to reflect the experimental objectives and an exchange algorithm is used to search across the design space for obtaining an efficient design when an exhaustive search is not possible. Our interest lies in estimating the direct comparisons among treatments, in the presence of nuisance network effects that stem from the underlying network interference structure governing the experimental units, or in the network effects themselves. Comparisons of optimal designs under different models, including the standard treatment models, are examined by comparing the variance and bias of treatment effect estimators. We also suggest a way of defining blocks, while taking into account the interrelations of groups of experimental units within a network, using spectral clustering techniques to achieve optimal modularity. We expect connected units within closed‐form communities to behave similarly to an external stimulus. We provide evidence that our approach can lead to efficiency gains over conventional designs such as randomised designs that ignore the network structure and we illustrate its usefulness for experiments on networks.EPSRC. Grant Number: EP/T021624/

Bayesian Optimal Design for Ordinary Differential Equation Models With Application in Biological Science

© 2019 The Author(s). Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of nonlinear ordinary differential equations. The motivation is an experiment to estimate parameters in the equations governing the transport of amino acids through cell membranes in human placentas. Decision-theoretic Bayesian design of experiments for such nonlinear models is conceptually very attractive, allowing the formal incorporation of prior knowledge to overcome the parameter dependence of frequentist design and being less reliant on asymptotic approximations. However, the necessary approximation and maximization of the, typically analytically intractable, expected utility results in a computationally challenging problem. These issues are further exacerbated if the solution to the differential equations is not available in closed-form. This article proposes a new combination of a probabilistic solution to the equations embedded within a Monte Carlo approximation to the expected utility with cyclic descent of a smooth approximation to find the optimal design. A novel precomputation algorithm reduces the computational burden, making the search for an optimal design feasible for bigger problems. The methods are demonstrated by finding new designs for a number of common models derived from differential equations, and by providing optimal designs for the placenta experiment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.The second author was supported by Fellowship EP/J018317/1 from the United Kingdom Engineering and Physical Sciences Research Council

A graph-theoretic framework for algorithmic design of experiments

In this paper, we demonstrate that considering experiments in a graph-theoretic manner allows us to exploit automorphisms of the graph to reduce the number of evaluations of candidate designs for those experiments, and thus find optimal designs faster. We show that the use of automorphisms for reducing the number of evaluations required of an optimality criterion function is effective on designs where experimental units have a network structure. Moreover, we show that we can take block designs with no apparent network structure, such as one-way blocked experiments, row-column experiments, and crossover designs, and add block nodes to induce a network structure. Considering automorphisms can thus reduce the amount of time it takes to find optimal designs for a wide class of experiments

New SOS diode pumping circuit based on an all-solid-state spiral generator for high-voltage nanosecond applications

Semiconductor opening switch (SOS) diodes are capable to switch currents with a density of more than 1 kA/cm 2 and withstand nanosecond pulses with an amplitude of up to 1 MV. SOS diodes, however, require a specific pumping circuit that must simultaneously provide forward and reverse pumping currents with a time of ∼ 500 and ∼ 100 ns, respectively. Such a pumping circuit with energies > 1 J typically requires a gas-discharge switch or a low-efficient solid-state solution. This study proposes a novel approach to pumping SOS diodes based on a spiral generator (SG) (also known as a vector inversion generator). Due to its wave characteristics, the SG produces a bipolar current discharge that meets the time duration and current amplitude required to pump an SOS diode. Moreover, the initial pulse from the spiral typically has a relatively low current amplitude compared to the opposite polarity secondary pulse, so the SOS diode can operate at very high efficiencies. This idea has been tested using an all-solid-state SG coupled with large-area SOS diodes (1 cm 2 ). With this combination, a voltage pulse of 62 kV having a rise time of only 11 ns was obtained on an open circuit load (3 pF, 1 M Ω ). The experiments were highly repeatable, with no damage to the components despite multiple tests. There is significant scope to further improve the results, with simple alterations to the SG

Design of Agricultural Field Experiments Accounting for both Complex Blocking Structures and Network Effects

Supplementary Information is available online at: https://link.springer.com/article/10.1007/s13253-023-00544-3#Sec13 .Copyright © 2023 The Author(s). We propose a novel model-based approach for constructing optimal designs with complex blocking structures and network effects for application in agricultural field experiments. The potential interference among treatments applied to different plots is described via a network structure, defined via the adjacency matrix. We consider a field trial run at Rothamsted Research and provide a comparison of optimal designs under various different models, specifically new network designs and the commonly used designs in such situations. It is shown that when there is interference between treatments on neighboring plots, designs incorporating network effects to model this interference are at least as efficient as, and often more efficient than, randomized row–column designs. In general, the advantage of network designs is that we can construct the neighbor structure even for an irregular layout by means of a graph to address the particular characteristics of the experiment. As we demonstrate through the motivating example, failing to account for the network structure when designing the experiment can lead to imprecise estimates of the treatment parameters and invalid conclusions.Supplementary materials accompanying this paper appear online.ESRC South Coast Doctoral Training Partnership, and the research was completed under EPSRC grant EP/T021624/1 Multi-Objective Optimal Design of Experiments

Designs with complex blocking structures and network effects for agricultural field experiments

Copyright 2021 The Author(s). We propose a novel model-based approach for constructing optimal designs with complex blocking structures and network effects, for application in agricultural field experiments. The potential interference among treatments applied to different plots is described via a network structure, defined via the adjacency matrix. We consider a field trial run at Rothamsted Research and provide a comparison of optimal designs under various different models, including the commonly used designs in such situations. It is shown that when there is interference between treatments on neighbouring plots, due to the spatial arrangement of the plots, designs incorporating network effects are at least as, and often more efficient than, randomised row-column designs. The advantage of network designs is that we can construct the neighbour structure even for an irregular layout by means of a graph to address the particular characteristics of the experiment. The need for such designs arises when it is required to account for treatment-induced patterns of heterogeneity. Ignoring the network structure can lead to imprecise estimates of the treatment parameters and invalid conclusions

Signal value of stress behaviour

Physiological and psychological stress are accompanied by nonverbal behaviour across a wide range of species. The function of this 'stress behaviour' is not well understood but is often assumed to be read by others as a cue to stress. Displaying signs of weakness is, however, difficult to understand from an evolutionary perspective and therefore further investigation into why these behaviours exist is needed. Here, we test whether displacement behaviours (i.e., those known to be associated with stress) are reliable indicators of stress in humans. To do this, we presented raters (N = 133) with videos of individuals (N = 31) undergoing a stress-inducting task. Self-directed displacement behaviours and self-reported stress were both associated with stress ratings given by raters. Therefore, such behaviours can provide reliable information to others and can be considered communicative. Individuals producing more nonverbal stress behaviour were rated as more likeable by raters (perhaps presenting as more honest signallers), indicating a benefit and potential adaptive function of displaying stress. Raters also differed in their accuracy in detecting stress from nonverbal cues. Findings suggest that the accuracy with which individuals were able to detect stress was linked to the number of social connections they reported to have. However, this association was non-linear, with individuals who were most and least accurate reporting the least network connections. This could indicate that the ability to read behaviour is associated with an ability to form and maintain social networks

Міжнародна наукова конференція "Архівознавство як наука"

Проаналізовано внесок істориків і архівістів України в розвиток архівної науки. Вказуються основні розділи архівознавства як науки, що вимагають подальших досліджень.Проанализирован вклад историков и архивистов Украины в развитие архивной науки. Указываются основные разделы архивоведения как науки, которые требуют дальнейших исследований.A contribution of Ukrainian historians and archivists to the archival science development is covered. The basic sections of the archival science which need the further study are indicated