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Data from: Optimizing stimulation and analysis protocols for neonatal fMRI

The development of brain function in young infants is poorly understood. The core challenge is that infants have a limited behavioral repertoire through which brain function can be expressed. Neuroimaging with fMRI has great potential as a way of characterizing typical development, and detecting abnormal development early. But, a number of methodological challenges must first be tackled to improve the robustness and sensitivity of neonatal fMRI. A critical one of these, addressed here, is that the hemodynamic response function (HRF) in pre-term and term neonates differs from that in adults, which has a number of implications for fMRI. We created a realistic model of noise in fMRI data, using resting-state fMRI data from infants and adults, and then conducted simulations to assess the effect of HRF of the power of different stimulation protocols and analysis assumptions (HRF modeling). We found that neonatal fMRI is most powerful if block-durations are kept at the lower range of those typically used in adults (full on/off cycle duration 25-30s). Furthermore, we show that it is important to use the age-appropriate HRF during analysis, as mismatches can lead to reduced power or even inverted signal. Where the appropriate HRF is not known (for example due to potential developmental delay), a flexible basis set performs well, and allows accurate post-hoc estimation of the HRF.

Data from: Mimicry among unequally defended prey should be mutualistic when predators sample optimally

Understanding the conditions under which moderately defended prey evolve to resemble better-defended prey and whether this mimicry is parasitic (quasi-Batesian) or mutualistic (Müllerian) is central to our understanding of warning signals. Models of predator learning generally predict quasi-Batesian relationships. However, predators' attack decisions are based not only on learning alone but also on the potential future rewards. We identify the optimal sampling strategy of predators capable of classifying prey into different profitability categories and contrast the implications of these rules for mimicry evolution with a classical Pavlovian model based on conditioning. In both cases, the presence of moderately unprofitable mimics causes an increase in overall consumption. However, in the case of the optimal sampling strategy, this increase in consumption is typically outweighed by the increase in overall density of prey sharing the model appearance (a dilution effect), causing a decrease in mortality. It suggests that if predators forage efficiently to maximize their long-term payoff, genuine quasi-Batesian mimicry should be rare, which may explain the scarcity of evidence for it in nature. Nevertheless, we show that when moderately defended mimics are profitable to attack by hungry predators, then they can be parasitic on their models, just as classical Batesian mimics are.

Data from: Optimal switching between geocentric and egocentric strategies in navigation

Animals use a combination of egocentric navigation driven by the internal integration of environmental cues, interspersed with geocentric course correction and reorientation. These processes are accompanied by uncertainty in sensory acquisition of information, planning and execution. Inspired by observations of dung beetle navigational strategies that show switching between geocentric and egocentric strategies, we consider the question of optimal reorientation rates for the navigation of an agent moving along a preferred direction in the presence of multiple sources of noise. We address this using a model that takes the form of a correlated random walk at short time scales that is punctuated by reorientation events leading to a biased random walks at long time scales. This allows us to identify optimal alternation schemes and characterize their robustness in the context of noisy sensory acquisition as well as performance errors linked with variations in environmental conditions and agent–environment interactions.

Data from: Locally and systemically induced glucosinolates follow optimal defence allocation theory upon root herbivory

1. Herbivore-induced defences in plants are considered a strategy to manage multiple interactions while saving resources. The optimal defence theory (ODT) is one of the most prominent theoretical frameworks to explain the defence allocation patterns within plants. It was recently shown that the ODT generally applies to constitutive glucosinolate (GSL) allocation in shoot and root organs. Previous studies showed that both root and shoot herbivore feeding may alter defence allocation over plant organs. For shoots, the effect depends on where the herbivores feed. It is as yet unknown whether similar principles apply to root-herbivore induced GSLs. 2. To analyse the effects of root localized herbivore feeding on GSL allocation, we conducted a pot experiment using Anomala cuprea grubs and four Brassicaceae; Brassica rapa, B. nigra, B. oleracea, and Sinapis alba. Individuals of these four plant species were grown in dedicated mesocosms. The grubs were confined either to the bottom soil, the middle section, or to the top soil. Plants grown in the same set-ups but without root herbivores served as controls. Glucosinolate levels of the leaf lamina, petiole, and stem as well as of the taproot, lateral roots, and fine roots were measured after eight days of herbivory. 3. Plant biomass reduction due to herbivory was the largest when herbivores were confined to the top soil. In the three Brassica species, taproot GSL levels increased upon herbivory independent of where the root herbivores were feeding. Glucosinolate levels in fine roots and shoots, on the other hand, hardly responded to root herbivory. Indole GSLs, which are more effective to pathogens than to herbivores, were more strongly induced than aliphatic and aromatic GSLs, especially in the taproots. Sinapis alba did not show remarkable increments in any GSL level upon herbivory. 4. These results show that locally and systemically induced defences in roots are consistent with the ODT: the taproot which is the most vulnerable and valuable to plant performance shows the highest increase in defence induction. The induced GSL profiles suggest that the response may not only target herbivores, but may also help to prevent secondary infection by microbial pathogens.

Data from: Integrating encounter theory with decision analysis to evaluate collision risk and determine optimal protection zones for wildlife

1. Better understanding human-wildlife interactions and their links with management can help improve the design of wildlife protection zones. One important example is the problem of wildlife collisions with vehicles or human-built structures (e.g. power lines, wind farms). In fact, collisions between marine wildlife and watercraft are among the major threats faced by several endangered species of marine mammals. Natural resource managers are therefore interested in finding cost-effective solutions to mitigate these threats. 2. We combined abundance estimators with encounter rate theory to estimate relative lethal collision risk of the Florida manatee (Trichechus manatus latirostris) from watercraft. We first modeled seasonal abundance of watercraft and manatees using a Bayesian analysis of aerial survey count data. We then modeled relative lethal collision risk in space and across seasons. Finally, we applied decision analysis and Linear Integer Programming to determine the optimal design of speed zones in terms of relative risk to manatees and costs to waterway users. We used a Pareto efficient frontier approach to evaluate the performance of alternative zones, which included additional practical considerations (e.g. spatial aggregation of speed zones) in relation to the optimal zone configurations. 3. Under the various relationships for probability of death given strike speed that we considered, the current speed zones reduced the relative lethal collision risk by an average of 51.5% to 70% compared to the scenario in which all speed regulations were removed (i.e. the no-protection scenario). We identified optimal zones and near-optimal zones with additional management considerations that improved upon the current zones in terms of cost or relative risk. 4. Policy Implications: Our analytical framework combines encounter rate theory and decision analysis to quantify the effectiveness of speed zones protecting manatees while accounting for uncertainty. Our approach can be used to optimize the design of protection zones intended to reduce conflicts between human waterborne activity and marine mammals. This framework could be extended to address many other problems of human-wildlife interactions, such as the optimal placement of wind farms to minimize collisions with wildlife or the optimal allocation of ranger effort to mitigate poaching threats.

opencc-zeroDec 2010View details →

Data from: Optimal lineage principle for age-structured populations

We present a formulation of branching and aging processes that allows distributions along lineages to be studied within populations, and provides a new interpretation of classical results in the theory of aging. We establish a variational principle for the stable age distribution along lineages. Using this optimal lineage principle, we show that the response of a population's growth rate to age-specific changes in mortality and fecundity – a key quantity which was first calculated by Hamilton – is given directly by the age distribution along lineages. We apply our method also to the Bellman-Harris process, in which both mother and progeny are rejuvenated at each reproduction event, and show that this process can be mapped to the classic aging process such that age statistics in the population and along lineages are identical. Our approach provides both a theoretical framework for understanding the statistics of aging in a population, and a new method of analytical calculations for populations with age structure. We discuss generalizations for populations with multiple phenotypes, and more complex aging processes. We also provide a first experimental test of our theory applied to bacterial populations growing in a microfluidics device.

Data from: Optimization and kinetic study of methyl laurate synthesis using ionic liquid [Hnmp]HSO4 as a catalyst

Methyl laurate was synthesized from lauric acid (LA) and methanol via an esterification reaction using ionic liquids (ILs) as catalysts. The efficiencies of three different catalysts, 1-methylimidazole hydrogen sulfate ([Hmim]HSO4), 1-methyl-2-pyrrolidonium hydrogen sulfate ([Hnmp]HSO4) and H2SO4, were compared. The effect of the methanol/LA molar ratio, reaction temperature, reaction time and catalyst dosage on the esterification rate of LA was investigated by single-factor experiments. Based on the single-factor experiments, the esterification of LA and methanol was optimized using response surface methodology. The results showed that the most effective catalyst was the IL [Hnmp]HSO4. The optimal conditions were as follows: [Hnmp]HSO4 dosage of 5.23%, methanol/LA molar ratio of 7.68 : 1, reaction time of 2.27 h and reaction temperature of 70°C. Under these conditions, the LA conversion of the esterification reached 98.58%. A kinetic study indicated that the esterification was a second-order reaction with an activation energy and a frequency factor of 68.45 kJ mol−1 and 1.9189 × 109 min−1, respectively. The catalytic activity of [Hnmp]HSO4 remained high after five cycles.

Data from: Quantification and optimization of ADF-STEM image contrast for beam sensitive materials

Many functional materials are difficult to analyze by Scanning Transmission Electron Microscopy (STEM) on account of their beam sensitivity and low contrast between different phases. The problem becomes even more severe when thick specimens need to be investigated, a situation that is common for materials that are ordered from the nanometer to micrometer length scales or when performing dynamic experiments in a TEM liquid cell. Here we report a method to optimize annular dark-field (ADF) STEM imaging conditions and detector geometries for thick and beam-sensitive low-contrast specimen using the example of a carbon nanotube/polymer nanocomposite. We carried-out Monte Carlo simulations as well as quantitative ADF-STEM imaging experiments to predict and verify optimum contrast conditions. The presented method is general, can be easily adapted to other beam-sensitive and/or low-contrast materials, as shown for a polymer vesicle within a TEM liquid-cell, and can act as an expert guide on whether an experiment is feasible and to determine the best imaging conditions.

Data from: Dynamics of pre-choice and post-choice behaviour: rats approximate optimal strategy in a discrete-trial decision task

We simulate two types of environments to investigate how closely rats approximate optimal foraging. Rats initiated a trial where they chose between two spouts for sucrose, which was delivered at distinct probabilities. The discrete trial procedure used allowed us to observe the relationship between choice proportions, response latencies and obtained rewards. Our results show that rats approximate the optimal strategy across a range of environments that differ in the average probability of reward as well as the dynamics of the depletion-renewal cycle. We found that the constituent components of a single choice differentially reflect environmental contingencies. Post-choice behaviour, measured as the duration of time rats spent licking at the spouts on unrewarded trials, was the most sensitive index of environmental variables, adjusting most rapidly to changes in the environment. These findings have implications for the role of confidence in choice outcomes for guiding future choices.

Data from: Predicting optimal outcomes in cognitive therapy or interpersonal psychotherapy for depressed individuals using the Personalized Advantage Index approach

Introduction: Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT). Methods: 134 depressed patients completed the pre- and post-treatment BDI-II assessment. First, we identified baseline predictors and moderators. Secondly, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach. Results: We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51). Discussion: Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI approach will need to be evaluated in prospective research, this study promotes the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.

opencc-zeroMar 2020View details →

Optimal control approaches for combining medicines and mosquito control in tackling dengue

Dengue is a debilitating and devastating viral infection spread by mosquito vectors, and over half the world's population currently live at risk of dengue (and other flavivirus) infections. Here we use an integrated epidemiological and vector ecology framework to predict optimal approaches for tackling dengue. Our aim is to investigate how vector control and/or vaccination strategies can be best combined and implemented for dengue disease control on small networks, and whether these optimal strategies differ under different circumstances. We show that that a combination of vaccination programmes and the release of genetically modified self-limiting mosquitoes (comparable to sterile insect approaches) is always considered the most beneficial strategy for reducing the number of infected individuals, due to both methods having differing impacts on the underlying disease dynamics. Additionally, depending on the impact of human movement on the disease dynamics, the optimal way to combat the spread of dengue is to focus prevention efforts on large population centres. Using mathematical frameworks, such as optimal control, are essential in developing predictive management and mitigation strategies for dengue disease control.

opencc-zeroDec 2013View details →

Data from: Comparison and optimization of hiPSC forebrain cortical differentiation protocols

Several protocols have been developed for human induced pluripotent stem cell neuronal differentiation. We compare several methods for forebrain cortical neuronal differentiation by assessing cell morphology, immunostaining and gene expression. We evaluate embryoid aggregate vs. monolayer with dual SMAD inhibition differentiation protocols, manual vs. AggreWell aggregate formation, plating substrates, neural progenitor cell (NPC) isolation methods, NPC maintenance and expansion, and astrocyte co-culture. The embryoid aggregate protocol, using a Matrigel substrate, consistently generates a high yield and purity of neurons. NPC isolation by manual selection, enzymatic rosette selection, or FACS all are efficient, but exhibit some differences in resulting cell populations. Expansion of NPCs as neural aggregates yields higher cell purity than expansion in a monolayer. Finally, co-culture of iPSC-derived neurons with astrocytes increases neuronal maturity by day 40. This study directly compares commonly employed methods for neuronal differentiation of iPSCs, and can be used as a resource for choosing between various differentiation protocols.

Data from: Thioflavin T as an amyloid dye: fibril quantification, optimal concentration and effect on aggregation

Formation of amyloid fibrils underlies a wide range of human disorders, including Alzheimer's and prion diseases. The amyloid fibrils can be readily detected thanks to thioflavin T (ThT), a small molecule that gives strong fluorescence upon binding to amyloids. Using the amyloid fibrils of Aβ40 and Aβ42 involved in Alzheimer's disease, and of yeast prion protein Ure2, here we study three aspects of ThT binding to amyloids: quantification of amyloid fibrils using ThT, the optimal ThT concentration for monitoring amyloid formation and the effect of ThT on aggregation kinetics. We show that ThT fluorescence correlates linearly with amyloid concentration over ThT concentrations ranging from 0.2 to 500 µM. At a given amyloid concentration, the plot of ThT fluorescence versus ThT concentration exhibits a bell-shaped curve. The maximal fluorescence signal depends mostly on the total ThT concentration, rather than amyloid to ThT ratio. For the three proteins investigated, the maximal fluorescence is observed at ThT concentrations of 20–50 µM. Aggregation kinetics experiments in the presence of different ThT concentrations show that ThT has little effect on aggregation at concentrations of 20 µM or lower. ThT at concentrations of 50 µM or more could affect the shape of the aggregation curves, but this effect is protein-dependent and not universal.

opencc-zeroDec 2013View details →

Data from: Parental optimism versus parental pessimism in plants: how common should we expect pollen limitation to be?

Many organisms exhibit parental optimism, producing more of the initial stages of offspring (e.g., eggs, embryos) than they can usually mature. For plants, parental optimism may be linked to the risk of seed production being limited by pollen receipt (pollen limitation). Here we elaborate a stochastic model of pollen limitation developed by Haig and Westoby (1988) and Burd (2008) and link it with published data on the magnitudes of prepollination costs versus postpollination costs of seed production in 80 plant species. We demonstrate that parental optimism should be expected when prepollination costs of seed production are small relative to postpollination costs. This was observed for most (62 of 80) of the plant taxa surveyed. Under parental optimism, plants overinvest in securing fertilized ovules, and consequently pollen limitation is predicted to be uncommon. However, for a sizable minority of plant species (18 of 80), prepollination costs approach or exceed postpollination costs. For these species, parental pessimism is instead optimal. Parents initiate fewer zygotes than they can usually mature, and pollen limitation is predicted to be severe. Because the relative magnitudes of prepollination and postpollination costs vary by more than 1,000-fold across plant taxa, parental outlook (optimism vs. pessimism) and levels of pollen limitation are predicted to vary widely.

Data from: Optimizing mouse surgery with online rectal temperature monitoring and preoperative heat supply. Effects on post-ischemic acute kidney injury.

Body temperature affects outcomes of tissue injury. We hypothesized that online body core temperature recording and selective interventions help to standardize peri-interventional temperature control and the reliability of outcomes in experimental renal ischemia reperfusion injury (IRI). We recorded core temperature in up to seven mice in parallel using a Thermes USB recorder and ret-3-iso rectal probes with three different protocols. Setup A: Heating pad during ischemia time; Setup B: Heating pad from incision to wound closure; Setup C: A ventilated heating chamber before surgery and during ischemia time with surgeries performed on a heating pad. Temperature profile recording displayed significant declines upon installing anesthesia. The profile of the baseline experimental setup A revealed that <1 % of the temperature readings were within the target range of 36.5 to 38.5 °C. Setup B and C increased the target range readings to 34.6 ± 28.0 % and 99.3 ± 1.5 %, respectively. Setup C significantly increased S3 tubular necrosis, neutrophil influx, and mRNA expression of kidney injury markers. In addition, using setup C different ischemia times generated a linear correlation with acute tubular necrosis parameters at a low variability, which further correlated with the degree of kidney atrophy 5 weeks after surgery. Changing temperature control setup A to C was equivalent to 10 minutes more ischemia time. We conclude that body temperature drops quickly in mice upon initiating anesthesia. Immediate heat supply, e.g. in a ventilated heating chamber, and online core temperature monitoring can help to standardize and optimize experimental outcomes.

opencc-zeroJul 2019View details →

Data from: Catalytic synthesis of n-butyl carboxylate with immobilized ionic liquid based on RSM optimization

Catalytic synthesis of n-butyl formate, n-butyl acetate, n-butyl propionate and n-butyl butyrate using four synthesized functional ionic liquids, 1-(3-sulfonapropyl)-1-methyl pyrrolidone sulfate hydrogen salt ([C3SO3Hnmp]HSO4), 1-(3-sulfopropyl)-1-methylpyrrolidone p-toluene sulfonate ([C3SO3Hnmp]CH3SO3H), 1-(3-sulfopropyl)-1-methyl pyrrolidone methyl sulfonate ([C3SO3Hnmp]C6H6SO3H), and 1-(3-sulfopropyl)-1-methyl pyrrolidone phosphate ([C3SO3Hnmp]H2PO4) was studied. Butyl butyrate was selected as the research object. The ionic liquid 1-(3-sulfopropyl)-1-methylpyrrolidone bisulfate ([C3SO3Hnmp]HSO4), which had the best catalytic effect on butyl butyrate, was immobilized and tested. The effects of reaction temperature, reaction time, molar ratio of acid to alcohol and catalyst dosage were investigated. Response surface methodology (RSM) was used to optimize the process conditions, and the optimal process conditions were obtained. The supported ionic liquid [C3SO3Hnmp]HSO4 maintained high catalytic activity after 5 cycles.

opencc-zeroDec 2013View details →

Data from: Fluid-dynamic optimal design of helical vascular graft for stenotic disturbed flow

Although a helical configuration of a prosthetic vascular graft appears to be clinically beneficial in suppressing thrombosis and intimal hyperplasia, an optimization of a helical design has yet to be achieved because of the lack of a detailed understanding on hemodynamic features in helical grafts and their fluid dynamic influences. In the present study, the swirling flow in a helical graft was hypothesized to have beneficial influences on a disturbed flow structure such as stenotic flow. The characteristics of swirling flows generated by helical tubes with various helical pitches and curvatures were investigated to prove the hypothesis. The fluid dynamic influences of these helical tubes on stenotic flow were quantitatively analysed by using a particle image velocimetry technique. Results showed that the swirling intensity and helicity of the swirling flow have a linear relation with a modified Germano number (Gn*) of the helical pipe. In addition, the swirling flow generated a beneficial flow structure at the stenosis by reducing the size of the recirculation flow under steady and pulsatile flow conditions. Therefore, the beneficial effects of a helical graft on the flow field can be estimated by using the magnitude of Gn*. Finally, an optimized helical design with a maximum Gn* was suggested for the future design of a vascular graft.

opencc-zeroDec 2016View details →

Data from: Optimal management strategy of insecticide resistance under various insect life histories: heterogeneous timing of selection and inter-patch dispersal

Although theoretical studies have shown that the mixture strategy, which uses multiple toxins simultaneously, can effectively delay the evolution of insecticide resistance, whether it is the optimal management under different insect life histories and insecticide types remains unknown. To test the robustness of the management strategy over the life histories, we developed a series of simulation models which cover almost all the diploid insect types and have the same basic structure describing pest population dynamics and resistance evolution with discrete time-steps. For each of two insecticidal toxins, one-locus two-allele autosomal inheritance of resistance was assumed. The simulations demonstrated the optimality of the mixture strategy either when insecticide efficacy was incomplete or when some part of the population disperses between patches before mating. The rotation strategy, which uses one insecticide on one pest generation and a different one on the next, did not differ from sequential usage in the time to resistance, except when dominance was low. It was the optimal strategy when insecticide efficacy was high and pre-mating selection and dispersal occur.

Data from: Do humans optimally exploit redundancy to control step variability in walking?

It is widely accepted that humans and animals minimize energetic cost while walking. While such principles predict average behavior, they do not explain the variability observed in walking. For robust performance, walking movements must adapt at each step, not just on average. Here, we propose an analytical framework that reconciles issues of optimality, redundancy, and stochasticity. For human treadmill walking, we defined a goal function to formulate a precise mathematical definition of one possible control strategy: maintain constant speed at each stride. We recorded stride times and stride lengths from healthy subjects walking at five speeds. The specified goal function yielded a decomposition of stride-to-stride variations into new gait variables explicitly related to achieving the hypothesized strategy. Subjects exhibited greatly decreased variability for goal-relevant gait fluctuations directly related to achieving this strategy, but far greater variability for goal-irrelevant fluctuations. More importantly, humans immediately corrected goal-relevant deviations at each successive stride, while allowing goal-irrelevant deviations to persist across multiple strides. To demonstrate that this was not the only strategy people could have used to successfully accomplish the task, we created three surrogate data sets. Each tested a specific alternative hypothesis that subjects used a different strategy that made no reference to the hypothesized goal function. Humans did not adopt any of these viable alternative strategies. Finally, we developed a sequence of stochastic control models of stride-to-stride variability for walking, based on the Minimum Intervention Principle. We demonstrate that healthy humans are not precisely "optimal," but instead consistently slightly over-correct small deviations in walking speed at each stride. Our results reveal a new governing principle for regulating stride-to-stride fluctuations in human walking that acts independently of, but in parallel with, minimizing energetic cost. Thus, humans exploit task redundancies to achieve robust control while minimizing effort and allowing potentially beneficial motor variability.