Nanoparticle- and microparticle-based luminescence imaging of chemical species and temperature in aquatic systems: a review

© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. Most aquatic systems rely on a multitude of biogeochemical processes that are coupled with each other in a complex and dynamic manner. To understand such processes, minimally invasive analytical tools are required that allow continuous, real-time measurements of individual reactions in these complex systems. Optical chemical sensors can be used in the form of fiber-optic sensors, planar sensors, or as micro- and nanoparticles (MPs and NPs). All have their specific merits, but only the latter allow for visualization and quantification of chemical gradients over 3D structures. This review (with 147 references) summarizes recent developments mainly in the field of optical NP sensors relevant for chemical imaging in aquatic science. The review encompasses methods for signal read-out and imaging, preparation of NPs and MPs, and an overview of relevant MP/NP-based sensors. Additionally, examples of MP/NP-based sensors in aquatic systems such as corals, plant tissue, biofilms, sediments and water-sediment interfaces, marine snow and in 3D bioprinting are given. We also address current challenges and future perspectives of NP-based sensing in aquatic systems in a concluding section. [Figure not available: see fulltext.]

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in 'hidden' physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Seagrass rhizosphere microenvironment alters plant-associated microbial community composition

© 2018 Society for Applied Microbiology and John Wiley & Sons Ltd The seagrass rhizosphere harbors dynamic microenvironments, where plant-driven gradients of O2 and dissolved organic carbon form microhabitats that select for distinct microbial communities. To examine how seagrass-mediated alterations of rhizosphere geochemistry affect microbial communities at the microscale level, we applied 16S rRNA amplicon sequencing of artificial sediments surrounding the meristematic tissues of the seagrass Zostera muelleri together with microsensor measurements of the chemical conditions at the basal leaf meristem (BLM). Radial O2 loss (ROL) from the BLM led to ∼ 300 µm thick oxic microzones, wherein pronounced decreases in H2S and pH occurred. Significantly higher relative abundances of sulphate-reducing bacteria were observed around the meristematic tissues compared to the bulk sediment, especially around the root apical meristems (RAM; ∼ 57% of sequences). Within oxic microniches, elevated abundances of sulphide-oxidizing bacteria were observed compared to the bulk sediment and around the RAM. However, sulphide oxidisers within the oxic microzone did not enhance sediment detoxification, as rates of H2S re-oxidation here were similar to those observed in a pre-sterilized root/rhizome environment. Our results provide novel insights into how chemical and microbiological processes in the seagrass rhizosphere modulate plant-microbe interactions potentially affecting seagrass health

Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data

Background: For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects. Objective: We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding. Methods: For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping. Results: If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression. Conclusion: Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Low oxygen affects photophysiology and the level of expression of two-carbon metabolism genes in the seagrass <i>Zostera muelleri</i>

© 2017, Springer Science+Business Media B.V. Seagrasses are a diverse group of angiosperms that evolved to live in shallow coastal waters, an environment regularly subjected to changes in oxygen, carbon dioxide and irradiance. Zostera muelleri is the dominant species in south-eastern Australia, and is critical for healthy coastal ecosystems. Despite its ecological importance, little is known about the pathways of carbon fixation in Z. muelleri and their regulation in response to environmental changes. In this study, the response of Z. muelleri exposed to control and very low oxygen conditions was investigated by using (i) oxygen microsensors combined with a custom-made flow chamber to measure changes in photosynthesis and respiration, and (ii) reverse transcription quantitative real-time PCR to measure changes in expression levels of key genes involved in C4 metabolism. We found that very low levels of oxygen (i) altered the photophysiology of Z. muelleri, a characteristic of C3 mechanism of carbon assimilation, and (ii) decreased the expression levels of phosphoenolpyruvate carboxylase and carbonic anhydrase. These molecular-physiological results suggest that regulation of the photophysiology of Z. muelleri might involve a close integration between the C3 and C4, or other CO2 concentrating mechanisms metabolic pathways. Overall, this study highlights that the photophysiological response of Z. muelleri to changing oxygen in water is capable of rapid acclimation and the dynamic modulation of pathways should be considered when assessing seagrass primary production

Optimal Swimming Speed in Head Currents and Effects on Distance Movement of Winter-Migrating Fish

Migration is a commonly described phenomenon in nature that is often caused by spatial and temporal differences in habitat quality. However, as migration requires energy, the timing of migration may depend not only on differences in habitat quality, but also on temporal variation in migration costs. Such variation can, for instance, arise from changes in wind or current velocity for migrating birds and fish, respectively. Whereas behavioural responses of birds to such changing environmental conditions have been relatively well described, this is not the case for fish, although fish migrations are both ecologically and economically important. We here use passive and active telemetry to study how winter migrating roach regulate swimming speed and distance travelled per day in response to variations in head current velocity. Furthermore, we provide theoretical predictions on optimal swimming speeds in head currents and relate these to our empirical results. We show that fish migrate farther on days with low current velocity, but travel at a greater ground speed on days with high current velocity. The latter result agrees with our predictions on optimal swimming speed in head currents, but disagrees with previously reported predictions suggesting that fish ground speed should not change with head current velocity. We suggest that this difference is due to different assumptions on fish swimming energetics. We conclude that fish are able to adjust both swimming speed and timing of swimming activity during migration to changes in head current velocity in order to minimize energy use

Physical activity levels as a quantifier in police officers and cadets

Objectives: The aim of the present study was to determine the physical activity levels of active duty police officers and police academy cadets in different life domains and intensities. These parameters were treated as potential quantifiers that could be used when assessing individuals preparing for work as future police officers. Material and Methods: The study recruited 153 active police officers and 176 cadets attending a police academy and administered a diagnostic survey, the long-form version of the International Physical Activity Questionnaire, while in the statistical analysis the Student's t-test for independent groups was applied. Results: It was determined that police officers present high physical activity levels within the work domain, which are developed from initial training at a police academy and then throughout their police career. Conclusions: Such data are important in the light of the role police officers play in public safety as well as the prominence of physical activity within a particular profession and how it can be targeted and tailored to their needs

Chironomid-based palaeotemperature estimates for northeast Finland during Oxygen Isotope Stage 3.

Quantitative palaeotemperature estimates for the earlier part of Oxygen Isotope Stage (OIS-) 3 are inferred from subfossil chironomid remains. The high-latitudinal study site of Sokli, northeast Finland, provides for a unique lacustrine deposit covering the earlier part of OIS-3, and the chironomid remains found in the sediments show that a shallow lake with a diverse fauna was present at the study site throughout the record. Using a Norwegian calibration data set as a modern analogue, mean July air temperatures are reconstructed. The chironomid-inferred July air temperatures are surprisingly high, reaching values similar to the current temperature at the study site. Other proxies that were applied to the sediments included the analysis of botanical and zoological macro-remains, and our results concur with temperature estimates derived from climate indicator taxa. Summer temperatures for interstadial conditions, reconstructed with climate models, are as high as our proxy-based palaeotemperatures