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Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution. In this paper, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al . that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution. For computations in low-dimensional distributions, the semi-analytical method is fast and thus should be considered. As the dimension increases, the Gessner et al . method becomes the only practically efficient approach of the methods tested here.

Submerged macrophytes can have a profound effect on shallow lake ecosystems through their ability to modify the thermal structure and dissolved oxygen levels within the lake. Invasive macrophytes, in particular, can grow rapidly and induce thermal gradients in lakes that may substantially change the ecosystem structure and challenge the survival of aquatic organisms. We performed fine-scale measurements and 3D numerical modeling at high spatiotemporal resolution to assess the effect of the seasonal growth of L. on the spatial and temporal dynamics of temperature and dissolved oxygen in a shallow urban lake (Lake Monger, Perth, WA, Australia). Daytime stratification developed during the growing season and was clearly observed throughout the macrophyte bed. At all times measured, stratification was stronger at the center of the macrophyte bed compared to the bed edges. By fitting a logistic growth curve to changes in plant height over time ( = 0.98), and comparing this curve to temperature data at the center of the macrophyte bed, we found that stratification began once the macrophytes occupied at least 50% of the water depth. This conclusion was strongly supported by a 3D hydrodynamic model fitted to weekly temperature profiles measured at four time periods throughout the growing season ( > 0.78 at all times). As the macrophyte height increased and stratification developed, dissolved oxygen concentration profiles changed from vertically homogeneous oxic conditions during both the day and night to expression of night-time anoxic conditions close to the sediments. Spatially interpolated maps of dissolved oxygen and 3D numerical modeling results indicated that the plants also reduced horizontal exchange with surrounding unvegetated areas, preventing flushing of low dissolved oxygen water out of the center of the bed. Simultaneously, aerial imagery showed central dieback occurring toward the end of the growing season. Thus, we hypothesized that stratification-induced anoxia can lead to accelerated dieback in this region, causing formation of a ring-shaped pattern in spatial macrophyte distribution. Overall, our study demonstrates that submerged macrophytes can alter the thermal characteristics and oxygen levels within shallow lakes and thus create challenging conditions for maximizing their spatial coverage.

Rising sea water temperature will play a significant role in responses of the world's seagrass meadows to climate change. In this study, we investigated seasonal and latitudinal variation (spanning more than 1,500 km) in seagrass productivity, and the optimum temperatures at which maximum photosynthesis and net productivity (for the leaf and the whole plant) occurs, for three seagrass species ( , and ). To obtain whole plant net production, photosynthesis, and respiration rates of leaves and the root/rhizome complex were measured using oxygen-sensitive optodes in closed incubation chambers at temperatures ranging from 15 to 43°C. The temperature-dependence of photosynthesis and respiration was fitted to empirical models to obtain maximum metabolic rates and thermal optima. The thermal optimum ( ) for gross photosynthesis of , which is more commonly distributed in sub-tropical to temperate regions, was 31°C. The for photosynthesis of the tropical species, and , was considerably higher (35°C on average). This suggests that seagrass species are adapted to water temperature within their distributional range; however, when comparing among latitudes and seasons, thermal optima within a species showed limited acclimation to ambient water temperature ( varied by 1°C in and 2°C in , and the variation did not follow changes in ambient water temperature). The for gross photosynthesis were higher than calculated from plant net productivity, which includes above- and below-ground respiration for (24°C) and 33°C), but remained unchanged at 35°C in . Both estimated plant net productivity and are sensitive to the proportion of below-ground biomass, highlighting the need for consideration of below- to above-ground biomass ratios when applying thermal optima to other meadows. The thermal optimum for plant net productivity was lower than ambient summer water temperature in , indicating likely contemporary heat stress. In contrast, thermal optima of and exceeded ambient water temperature. This study found limited capacity to acclimate: thus the thermal optima can forewarn of both the present and future vulnerability to ocean warming during periods of elevated water temperature.

Seagrasses are globally important coastal habitat-forming species, yet it is unknown how seagrasses respond to the combined pressures of ocean acidification and warming of sea surface temperature. We exposed three tropical species of seagrass (Cymodocea serrulata, Halodule uninervis, and Zostera muelleri) to increasing temperature (21, 25, 30, and 35°C) and pCO2 (401, 1014, and 1949 μatm) for 7 wk in mesocosms using a controlled factorial design. Shoot density and leaf extension rates were recorded, and plant productivity and respiration were measured at increasing light levels (photosynthesis–irradiance curves) using oxygen optodes. Shoot density, growth, photosynthetic rates, and plant-scale net productivity occurred at 25°C or 30°C under saturating light levels. High pCO2 enhanced maximum net productivity for Z. muelleri, but not in other species. Z. muelleri was the most thermally tolerant as it maintained positive net production to 35°C, yet for the other species there was a sharp decline in productivity, growth, and shoot density at 35°C, which was exacerbated by pCO2. These results suggest that thermal stress will not be offset by ocean acidification during future extreme heat events and challenges the current hypothesis that tropical seagrass will be a ‘winner’ under future climate change conditions.

PurposeQuantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confidence and improve their potential as a biomarker to monitor the progression of Parkinson’s disease (PD). In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques.ProceduresWe designed and trained artificial neural networks (ANNs) to analyze the data from 69 patients within the Parkinson’s Progressive Marker Initiative (PPMI) database. The task was to predict the unified PD rating scale (UPDRS) part III motor score in year 4 from 92 imaging features extracted on 12 different regions as well as 6 non-imaging measures at baseline (year 0). We first performed univariate screening (including the adjustment for false discovery) to select 4 regions each having 10 features with significant performance in classifying year 4 motor outcome into two classes of patients (divided by the UPDRS III threshold of 30). The leave-one-out strategy was then applied to train and test the ANNs for individual and combinations of features. The prediction statistics were calculated from 100 rounds of experiments, and the accuracy in appropriate prediction (classification of year 4 outcome) was quantified.ResultsOut of the baseline non-imaging features, only the UPDRS III (at year 0) was predictive, while multiple imaging features depicted significance. The different selected features reached a predictive accuracy of 70 % if used individually. Combining the top imaging features from the selected regions significantly improved the prediction accuracy to 75 % (p < 0.01). The combination of imaging features with the year 0 UPDRS III score also improved the prediction accuracy to 75 %.ConclusionThis study demonstrated the added predictive value of radiomic features extracted from DAT SPECT images in serving as a biomarker for PD progression tracking.

Forecasting ecosystem changes due to disturbances or conservation interventions is essential to improve ecosystem management and anticipate unintended consequences of conservation decisions. Mathematical models allow practitioners to understand the potential effects and unintended consequences via simulation. However, calibrating these models is often challenging due to a paucity of appropriate ecological data. Ensemble ecosystem modelling (EEM) is a quantitative method used to parameterize models from theoretical ecosystem features rather than data. Two approaches have been considered to find parameter values satisfying those features: a standard accept–reject algorithm, appropriate for small ecosystem networks, and a sequential Monte Carlo (SMC) algorithm that is more computationally efficient for larger ecosystem networks. In practice, using SMC for EEM generation requires advanced statistical and mathematical knowledge, as well as strong programming skills, which might limit its uptake. In addition, current EEM approaches have been developed for only one model structure (generalised Lotka–Volterra). To facilitate the usage of EEM methods, we introduce EEMtoolbox, an R package for calibrating quantitative ecosystem models. Our package allows the generation of parameter sets satisfying ecosystem features by using either the standard accept–reject algorithm or the novel SMC procedure. Our package extends the existing EEM methodology, originally developed for the generalised Lotka–Volterra model, to two additional model structures (the multispecies Gompertz and the Bimler–Baker model) and additionally allows users to define their own model structures. We demonstrate the usage of EEMtoolbox by simulating changes in species abundance immediately after the release of the sihek (Todiramphus cinnamominus, extinct‐in‐the‐wild species) on Palmyra Atoll in the Pacific Ocean. With its simple interface, our package facilitates straightforward generation of EEM parameter sets, thus unlocking advanced statistical methods supporting conservation decisions using ecosystem network models.

Light gradients are ubiquitous in marine systems as light reduces exponentially with depth. Seagrasses have a set of mechanisms that help them to cope with light stress gradients. Physiological photoacclimation and clonal integration help to maximize light capture and minimize carbon losses. These mechanisms can shape plants minimum light requirements (MLR), which establish critical thresholds for seagrass survival and help us predict ecosystem responses to the alarming reduction in light availability. Using the seagrass as a case study, we compare the MLR under different carbon model scenarios, which include photoacclimation and/or self-facilitation (based on clonal integration) and that where parameterized with values from field experiments. Physiological photoacclimation conferred plants with increased tolerance to reducing light, approximately halving their MLR from 5-6% surface irradiance (SI) to ≈ 3% SI. In oligotrophic waters, this change in MLR could translate to an increase of several meters in their depth colonization limit. In addition, we show that reduced mortality rates derived from self-facilitation mechanisms (promoted by high biomass) induce bistability of seagrass meadows along the light stress gradient, leading to abrupt shifts and hysteretic behaviors at their deep limit. The results from our models point to (i) the critical role of physiological photoacclimation in conferring greater resistance and ability to recover (i.e., resilience), to seagrasses facing light deprivation and (ii) the importance of self-facilitating reinforcing mechanisms in driving the resilience and recovery of seagrass systems exposed to severe light reduction events.

Tree stems swell and shrink daily, which is thought to reflect changes in the volume of water within stem tissues. We observed these daily patterns using automatic dendrometer bands in a diverse group of mangrove species over five mangrove forests across Australia and New Caledonia. We found that mangrove stems swelled during the day and shrank at night. Maximum swelling was highly correlated with daily maxima in air temperature. Variation in soil salinity and levels of tidal inundation did not influence the timing of stem swelling over all species. Medium-term increases in stem circumference were highly sensitive to rainfall. We defoliated trees to assess the role of foliar transpiration in stem swelling and shrinking. Defoliated trees showed maintenance of the pattern of daytime swelling, indicating that processes other than canopy transpiration influence the temporary stem diameter increments, which could include thermal swelling of stems. More research is required to understand the processes contributing to stem shrinking and swelling. Automatic Dendrometer Bands could provide a useful tool for monitoring the response of mangroves to extreme climatic events as they provide high-frequency, long-term, and large-scale information on tree water status.

We propose and mathematically examine a theory of calcium profile formation in unwounded mammalian epidermis based on: changes in keratinocyte proliferation, fluid and calcium exchange with the extracellular fluid during these cells' passage through the epidermal sublayers, and the barrier functions of both the stratum corneum and tight junctions localised in the stratum granulosum. Using this theory, we develop a mathematical model that predicts epidermal sublayer transit times, partitioning of the epidermal calcium gradient between intracellular and extracellular domains, and the permeability of the tight junction barrier to calcium ions. Comparison of our model's predictions of epidermal transit times with experimental data indicates that keratinocytes lose at least 87% of their volume during their disintegration to become corneocytes. Intracellular calcium is suggested as the main contributor to the epidermal calcium gradient, with its distribution actively regulated by a phenotypic switch in calcium exchange between keratinocytes and extracellular fluid present at the boundary between the stratum spinosum and the stratum granulosum. Formation of the extracellular calcium distribution, which rises in concentration through the stratum granulosum towards the skin surface, is attributed to a tight junction barrier in this sublayer possessing permeability to calcium ions that is less than 15 nm s-1 in human epidermis and less than 37 nm s-1 in murine epidermis. Future experimental work may refine the presented theory and reduce the mathematical uncertainty present in the model predictions.

Fiber-reinforced cementitious composites (FRCC) are a class of materials made by adding randomly distributed fibers to a cementitious matrix, providing better material toughness through the crack bridging behavior of the fibers. One of the primary concerns with FRCCs is the behavior of the fiber when a crack is formed. The fibers provide a stress-bridging mechanism, which is largely determined by the bond that exists between the concrete and the fiber’s outer surface. While many studies have determined the properties of FRCCs and potential benefits of using specific fiber types, the effects of low temperature or cold plasma treatment of polymer fibers on the mechanical behavior of the composite material are limited. Polymer fibers are notable for their low density, ductility, ease of manufacture, and cost-effectiveness. Despite these advantages, the surface properties of polymers do not enable the bonding potential given by steel or glass fibers when used in untreated FRCC, resulting in pull-out failures before the full displacement capacity of the fiber is utilized. For this reason, modification of the surface characteristics of polymer fibers can aid in higher bonding potential. Plasma treatment is a process wherein surfaces are modified through the kinetics of electrically charged and reactive species in a gaseous discharge environment. This paper is a preliminary study on the use of atmospheric pressure plasma generated at approximately room temperature. This atmospheric, cold plasma treatment is a method for improving the mechanical properties of FRCC using polymeric fibers. In this study, polypropylene and polyvinyl-alcohol fibers were cold plasma treated for 0, 30, 60, and 120 s before being used in cementitious mortar mixtures. Compression and flexure tests were performed using a displacement-based loading protocol to examine the impact of plasma treatment time on the corresponding mechanical performance of the fiber-reinforced cementitious composite. The experimental results obtained from this study indicate that there is a positive correlation between fiber treatment time and post-peak load-carrying capacity, especially for specimens subjected to flexural loading.