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Background Fear of falling (FOF) is prevalent among older adults and associated with adverse health outcomes. Over recent years a substantial body of research has emerged on its epidemiology, associated factors, and consequences. This scoping review summarizes the FOF literature published between April 2015 and March 2020 in order to inform current practice and identify gaps in the literature. Methods A total of 439 articles related to FOF in older adults were identified, 56 selected for full-text review, and 46 retained for data extraction and synthesis. Results The majority of included studies were cross-sectional. Older age, female sex, previous falls, worse physical performance, and depressive symptoms were the factors most consistently associated with FOF. Studies that measured FOF with a single question reported a significantly lower prevalence of FOF than those using the Falls Efficacy Scale, a continuous measure. FOF was associated with higher likelihoods of future falls, short-term mortality, and functional decline. Conclusions Comparisons between studies were limited by inconsistent definition and measurement of FOF, falls, and other characteristics. Consensus on how to measure FOF and which participant characteristics to evaluate would address this issue. Gaps in the literature include clarifying the relationships between FOF and cognitive, psychological, social, and environmental factors.

Increasing severity and frequency of drought is predicted for large portions of the terrestrial biosphere, with major impacts already documented in wet tropical forests. Using a 4-year rainfall exclusion experiment in the Daintree Rainforest in northeast Australia, we examined canopy tree responses to reduced precipitation and soil water availability by quantifying seasonal changes in plant hydraulic and carbon traits for 11 tree species between control and drought treatments. Even with reduced soil volumetric water content in the upper 1m of soil in the drought treatment, we found no significant difference between treatments for predawn and midday leaf water potential, photosynthesis, stomatal conductance, foliar stable carbon isotope composition, leaf mass per area, turgor loss point, xylem vessel anatomy, or leaf and stem nonstructural carbohydrates. While empirical measurements of aboveground traits revealed homeostatic maintenance of plant water status and traits in response to reduced soil moisture, modeled belowground dynamics revealed that trees in the drought treatment shifted the depth from which water was acquired to deeper soil layers. These findings reveal that belowground acclimation of tree water uptake depth may buffer tropical rainforests from more severe droughts that may arise in future with climate change.

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential (“risky” stomatal regulation), high xylem hydraulic conductivity, and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance, and low stomatal conductance at low leaf water potential (“conservative” stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits and their interactions (i.e., networks), has potential to improve crop growth predictions in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.Summary statementOur process-based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield.

Drought-associated woody-plant mortality has been increasing in most regions with multi-decadal records and is projected to increase in the future, impacting terrestrial climate forcing, biodiversity and resource availability. The mechanisms underlying such mortality, however, are debated, owing to complex interactions between the drivers and the processes. In this Review, we synthesize knowledge of drought-related tree mortality under a warming and drying atmosphere with rising atmospheric CO2. Drought-associated mortality results from water and carbon depletion and declines in their fluxes relative to demand by living tissues. These pools and fluxes are interdependent and underlay plant defences against biotic agents. Death via failure to maintain a positive water balance is particularly dependent on soil-to-root conductance, capacitance, vulnerability to hydraulic failure, cuticular water losses and dehydration tolerance, all of which could be exacerbated by reduced carbon supply rates to support cellular survival or the carbon starvation process. The depletion of plant water and carbon pools is accelerated under rising vapour pressure deficit, but increasing CO2 can mitigate these impacts. Advancing knowledge and reducing predictive uncertainties requires the integration of carbon, water and defensive processes, and the use of a range of experimental and modelling approaches., Enhanced drought frequency and magnitude have impacted tree mortality, leading to multiple examples of regional-scale dieback. This Review outlines the mechanisms leading to mortality, including carbon starvation and hydraulic failure.

BACKGROUND: African colorectal cancer (CRC) rates are rising rapidly. A low-cost CRC screening approach is needed to identify CRC from non-CRC patients who should be sent for colonoscopy (a scarcity in Africa). OBJECTIVE: To identify urinary metabolite biomarkers that, combined with easy-to-measure clinical variables, would identify patients that should be further screened for CRC by colonoscopy. Ideal metabolites would be water-soluble and easily translated into a sensitive, low-cost point-of-care (POC) test. METHODS: Liquid-chromatography mass spectrometry (LC-MS/MS) was used to quantify 142 metabolites in spot urine samples from 514 Nigerian CRC patients and healthy controls. Metabolite concentration data and clinical characteristics were used to determine optimal sets of biomarkers for identifying CRC from non-CRC subjects. RESULTS: Our statistical analysis identified N1, N12-diacetylspermine, hippurate, p-hydroxyhippurate, and glutamate as the best metabolites to discriminate CRC patients via POC screening. Logistic regression modeling using these metabolites plus clinical data achieved an area under the receiver-operator characteristic (AUCs) curves of 89.2% for the discovery set, and 89.7% for a separate validation set. CONCLUSIONS: Effective urinary biomarkers for CRC screening do exist. These results could be transferred into a simple, POC urinary test for screening CRC patients in Africa.

Physical activity (PA) and social support have known benefits for the well-being and health of older adults, and social support is associated with PA behavior and positive affective experiences in PA contexts. The aim of this study was to synthesize qualitative research conducted on the experiences of social support related to PA among older adults (age ≥55 years). Following meta-study methodology, the authors searched nine databases and extracted information from 31 studies. Results were synthesized in terms of common themes and in light of theoretical and methodological perspectives used. The qualitative literature identifies supportive behaviors and social network outcomes which may be useful for informing how best to support older adults to be physically active. This literature rarely reflected the experiences of vulnerable populations, and future research should aim to further understand supportive behaviors which enable older adults to overcome barriers and challenges to being physically active.

Crop improvement is crucial to ensuring global food security under climate change, and hence there is a pressing need for phenotypic observations that are both high throughput and improve mechanistic understanding of plant responses to environmental cues and limitations. In this study, chlorophyll a fluorescence light response curves and gas-exchange observations are combined to test the photosynthetic response to moderate drought in four genotypes of Brassica rapa. The quantum yield of PSII (ϕ(PSII)) is here analyzed as an exponential decline under changing light intensity and soil moisture. Both the maximum ϕ(PSII) and the rate of ϕ(PSII) decline across a large range of light intensities (0–1,000 μmol photons m(−2) s(−1); β(PSII)) are negatively affected by drought. We introduce an alternative photosynthesis model (β(PSII) model) incorporating parameters from rapid fluorescence response curves. Specifically, the model uses β(PSII) as an input for estimating the photosynthetic electron transport rate, which agrees well with two existing photosynthesis models (Farquhar-von Caemmerer-Berry and Yin). The β(PSII) model represents a major improvement in photosynthesis modeling through the integration of high-throughput fluorescence phenotyping data, resulting in gained parameters of high mechanistic value.

Increasing seawater exposure is killing coastal trees globally, with expectations of accelerating mortality with rising sea levels. However, the impact of concomitant changes in atmospheric CO

By utilizing functional relationships based on observations at plot or field scales, water quality models first compute surface runoff and then use it as the primary governing variable to estimate sediment and nutrient transport. When these models are applied at watershed scales, this serial model structure, coupling a surface runoff sub‐model with a water quality sub‐model, may be inappropriate because dominant hydrological processes differ among scales. A parallel modeling approach is proposed to evaluate how best to combine dominant hydrological processes for predicting water quality at watershed scales. In the parallel scheme, dominant variables of water quality models are identified based entirely on their statistical significance using time series analysis. Four surface runoff models of different model complexity were assessed using both the serial and parallel approaches to quantify the uncertainty on forcing variables used to predict water quality. The eight alternative model structures were tested against a 25‐year high‐resolution data set of streamflow, suspended sediment discharge, and phosphorous discharge at weekly time steps. Models using the parallel approach consistently performed better than serial‐based models, by having less error in predictions of watershed scale streamflow, sediment and phosphorus, which suggests model structures of water quantity and quality models at watershed scales should be reformulated by incorporating the dominant variables. The implication is that hydrological models should be constructed in a way that avoids stacking one sub‐model with one set of scale assumptions onto the front end of another sub‐model with a different set of scale assumptions.

Genomic prediction is used to parameterize canopy growth of a process-based whole-plant model. The updated model formalizes components of genotype by environment interaction (G×E)., Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first principles-based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L. to set the stage for future large-scale process-based modeling of intraspecific variation. We develop a new canopy growth submodel for B. rapa within the process-based model Terrestrial Regional Ecosystem Exchange Simulator (TREES), test input parameters for feasibility of direct estimation with observed phenotypes across cultivated morphotypes and indirect estimation using genomic prediction on a recombinant inbred line population, and explore model performance on an in silico population under non-stressed and mild water-stressed conditions. We find evidence that the updated whole-plant model has the capacity to distill genotype by environment interaction (G×E) into tractable components. The framework presented offers a means to link genetic variation with environment-modulated plant response and serves as a stepping stone towards large-scale prediction of unphenotyped, genetically related individuals under untested environmental scenarios.

Inadequate blood supply to the intestine can lead to acute mesenteric ischemia (AMI), with a mortality rate ranging from 60% to 90%. This high mortality rate is partially due to late detection and the lack of efficient early diagnostic tests. There is an urgent need for a point-of-care tool for immediate bedside diagnosis. Here we present for the first time a rapid and non-invasive electrochemical biosensor device based on non-faradic impedance spectroscopy to detect intestinal fatty-acid binding protein (I-FABP) as an indication of AMI. The electrochemical biosensors consist of gold interdigitated electrodes that were fabricated using photolithographic techniques on top of silicon dioxide substrates. The electrode surfaces were functionalized with an I-FABP capture antibody (CAnB) to entice the target protein, while gold nanoparticles (GNPs) functionalized with detection antibodies (DAnB-GNPs) were utilized as a novel mechanism to enhance the detection signal. Quantification of the I-FABP concentration in the medium depended on its attachment to CAnB and DAnB-GNPs in a sandwich manner, where the latter boosts the impedance signal through its binding to the I-FABP. This non-invasive non-faradic electric biosensor device demonstrates the potential for bench-to-bedside translation with the goal of decreasing morbidity and mortality from AMI.

Summary Understanding the genetic and physiological basis of abiotic stress tolerance under field conditions is key to varietal crop improvement in the face of climate variability. Here, we investigate dynamic physiological responses to water stress in silico and their relationships to genotypic variation in hydraulic traits of cotton (Gossypium hirsutum), an economically important species for renewable textile fiber production.In conjunction with an ecophysiological process‐based model, heterogeneous data (plant hydraulic traits, spatially‐distributed soil texture, soil water content and canopy temperature) were used to examine hydraulic characteristics of cotton, evaluate their consequences on whole plant performance under drought, and explore potential genotype × environment effects.Cotton was found to have R‐shaped hydraulic vulnerability curves (VCs), which were consistent under drought stress initiated at flowering. Stem VCs, expressed as percent loss of conductivity, differed across genotypes, whereas root VCs did not. Simulation results demonstrated how plant physiological stress can depend on the interaction between soil properties and irrigation management, which in turn affect genotypic rankings of transpiration in a time‐dependent manner.Our study shows how a process‐based modeling framework can be used to link genotypic variation in hydraulic traits to differential acclimating behaviors under drought.

Sclerotinia stem rot, caused by the fungal pathogen Sclerotinia sclerotiorum, is a destructive disease of canola and many other broadleaf crops. The primary inoculum responsible for initiating Sclerotinia epidemics is airborne ascospores released from the apothecia of sclerotia. Timely detection of the presence of airborne ascospores can serve as an early-warning system for forecasting and management of the disease. A major challenge is to develop a portable and automated device which can be deployed onsite to detect and quantify the presence of minute quantities of ascospores in the air and serves as a unit in a network of systems for forecasting of the epidemic. In this communication, we present the development of an impedimetric non-Faradaic biosensor based on anti-S. sclerotiorum polyclonal antibodies as probes to selectively capture the ascospores and sense their binding by an impedance based interdigitated electrode which was found to directly and unambiguously correlate the number of ascospores on sensor surface with the impedance response.

From 2011 to 2013, Texas experienced its worst drought in recorded history. This event provided a unique natural experiment to assess species-specific responses to extreme drought and mortality of four co-occurring woody species: Quercus fusiformis, Diospyros texana, Prosopis glandulosa, and Juniperus ashei. We examined hypothesized mechanisms that could promote these species' diverse mortality patterns using postdrought measurements on surviving trees coupled to retrospective process modelling. The species exhibited a wide range of gas exchange responses, hydraulic strategies, and mortality rates. Multiple proposed indices of mortality mechanisms were inconsistent with the observed mortality patterns across species, including measures of the degree of iso/anisohydry, photosynthesis, carbohydrate depletion, and hydraulic safety margins. Large losses of spring and summer whole-tree conductance (driven by belowground losses of conductance) and shallower rooting depths were associated with species that exhibited greater mortality. Based on this retrospective analysis, we suggest that species more vulnerable to drought were more likely to have succumbed to hydraulic failure belowground.

Forest mortality has been widely observed across the globe during recent episodes of drought and extreme heat events. But the future of forest mortality remains poorly understood. While the direct effects of future climate and elevated CO2 on forest mortality risk have been studied, the role of lateral subsurface water flow has rarely been considered. Here we demonstrated the fingerprint of lateral flow on the forest mortality risk of a riparian ecosystem using a coupled plant hydraulics-hydrology model prescribed with multiple Earth System Model projections of future hydroclimate. We showed that the anticipated water-saving and drought ameliorating effects of elevated CO2 on mortality risk were largely compromised when lateral hydrological processes were considered. Further, we found lateral flow reduce ecosystem sensitivity to climate variations, by removing soil water excess during wet periods and providing additional water from groundwater storage during dry periods. These findings challenge the prevailing expectation of elevated CO2 to reduce mortality risk and highlight the need to assess the effects of lateral flow exchange more explicitly moving forward with forest mortality projections.

Mountain pine beetle outbreaks in western North America have led to extensive forest mortality, justifiably generating interest in improving our understanding of how this type of ecological disturbance affects hydrological cycles. While observational studies and simulations have been used to elucidate the effects of mountain beetle mortality on hydrological fluxes, an ecologically-mechanistic model of forest evapotranspiration (ET) evaluated against field data has yet to be developed. In this work, we use the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to incorporate the ecohydrological impacts of mountain pine beetle disturbance on ET for a lodgepole pine-dominated forest equipped with an eddy covariance tower. An existing degree-day model was incorporated that predicted the life cycle of mountain pine beetles, along with an empirically-derived submodel that allowed sap flux to decline as a function of temperature-dependent blue stain fungal growth. The eddy covariance footprint was divided into multiple cohorts for multiple growing seasons, including representations of recently attacked trees and the compensatory effects of regenerating understory, using two different spatial scaling methods. Our results showed that using a multiple cohort approach matched eddy covariance-measured ecosystem-scale ET fluxes well, and showed improved performance compared to model simulations assuming a binary framework of only areas of live and dead overstory. Cumulative growing season ecosystem-scale ET fluxes were 8 – 29% greater using the multi-cohort approach during years in which beetle attacks occurred, highlighting the importance of including compensatory ecological mechanism in ET models.

Stomatal regulation presumably evolved to optimize CO2 for H2 O exchange in response to changing conditions. If the optimization criterion can be readily measured or calculated, then stomatal responses can be efficiently modelled without recourse to empirical models or underlying mechanism. Previous efforts have been challenged by the lack of a transparent index for the cost of losing water. Yet it is accepted that stomata control water loss to avoid excessive loss of hydraulic conductance from cavitation and soil drying. Proximity to hydraulic failure and desiccation can represent the cost of water loss. If at any given instant, the stomatal aperture adjusts to maximize the instantaneous difference between photosynthetic gain and hydraulic cost, then a model can predict the trajectory of stomatal responses to changes in environment across time. Results of this optimization model are consistent with the widely used Ball-Berry-Leuning empirical model (r2 > 0.99) across a wide range of vapour pressure deficits and ambient CO2 concentrations for wet soil. The advantage of the optimization approach is the absence of empirical coefficients, applicability to dry as well as wet soil and prediction of plant hydraulic status along with gas exchange.

Effects of future large summer storm events due to climate change on vegetation carbon metabolism across western United States remains poorly understood. Canopy carbon metabolism of sagebrush (Artemisia tridentata) was evaluated for 7 days during the two driest months (July and August) by irrigating sagebrush plots with 20 mm precipitation pulses. Due to its dimorphic rooting system, we hypothesized sustained response to large precipitation events. Photosynthesis (An) and stomatal conductance (gs) peaked within 2–3 days of irrigation and returned to pre-irrigation values by day 7. Predawn water potential (Ψpd) peaked within 1 day and returned to its pre-pulse value by day 3 while potential quantum efficiency for light adapted leaves (Fv’/Fm’) as well as intrinsic water use efficiency (WUEi) was unresponsive. Unlike leaves, fine roots in the top 30 cm of soil were not a carbon sink. Heterotrophic respiration (Rh) was the dominant contributor to total soil respiration (Rs), and peaked within 24 h before it dropped to pre-pulse value by day 3. Different environmental drivers regulated Rs and Rh, highlighting different kinetics of carbon production. Our study suggests ephemeral response of cold desert vegetation to future large summer storm events with important implications for the overall carbon storage capacity.

Developing a real-time, portable, and inexpensive sensor for pathogenic bacteria is crucial since the conventional detection approaches such as enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) assays are high cost, time-consuming, and require an expert operator. Here we present a portable, inexpensive, and convenient impedance-based biosensor using Interdigitated Electrode (IDE) arrays to detect Escherichia coli (E. coli) as a model to demonstrate the feasibility of an impedance-based biosensor. We manipulated the affinity of the IDE array towards E. coli (E. coli BL21 series) by functionalizing the IDEs’ surface with an E. coli outer membrane protein (OMP) Ag1 Aptamer. To determine the dominant factors affecting the sensitivity and the performance of the biosensor in detecting E. coli, we investigated the roles of the substrate material used in the fabrication of the IDE, the concentration of the aptamer, and the composition of the carboxy aliphatic thiol mixture used in the pre-treatment of the IDE surface. In the sensing experiments we used an E. coli concentration range of 25–1000 cfu mL−1 and confirmed the binding of the OMP Ag1 Aptamer to the outer membrane protein of the E. coli by Field Emission Scanning Electron Microscopy (FESEM), Optical Microscopy, and Atomic Force Microscopy (AFM). By tuning the surface chemistry, the IDEs’ substrate material, and the concentration of the OMP Ag1 Aptamer, our sensor could detect E. coli with the analytical sensitivity of approximately 1.8 Ohm/cfu and limit of detection of 9 cfu mL−1. We found that the molecular composition of the self-assembled monolayer (SAM) formed on the top of the IDEs before the attachment of the OMP Ag1 Aptamer significantly impacted the sensitivity of the sensor. Notably, with straightforward changes to the molecular recognition elements, this platform device can be used to detect a wide range of other microorganisms and chemicals relevant for environmental monitoring and public health.

Trees may survive prolonged droughts by shifting water uptake to reliable water sources, but it is unknown if the dominant mechanism involves activating existing roots or growing new roots during drought, or some combination of the two. To gain mechanistic insights on this unknown, a dynamic root-hydraulic modeling framework was developed that set up a feedback between hydraulic controls over carbon allocation and the role of root growth on soil-plant hydraulics. The new model was tested using a 5 yr drought/heat field experiment on an established pinon-juniper stand with root access to bedrock groundwater. Owing to the high carbon cost per unit root area, modeled trees initialized without adequate bedrock groundwater access experienced potentially lethal declines in water potential, while all of the experimental trees maintained nonlethal water potentials. Simulated trees were unable to grow roots rapidly enough to mediate the hydraulic stress, particularly during warm droughts. Alternatively, modeled trees initiated with root access to bedrock groundwater matched the hydraulics of the experimental trees by increasing their water uptake from bedrock groundwater when soil layers dried out. Therefore, the modeling framework identified a critical mechanism for drought response that required trees to shift water uptake among existing roots rather than growing new roots.