Collagen chirality and three‐dimensional orientation studied with polarimetric second‐harmonic generation microscopy

Polarization‐dependent second‐harmonic generation (P‐SHG) microscopy is used to characterize molecular nonlinear optical properties of collagen and determine a three‐dimensional (3D) orientation map of collagen fibers within a pig tendon. C₆ symmetry is used to determine the nonlinear susceptibility tensor components ratios in the molecular frame of reference and , where the latter is a newly extracted parameter from the P‐SHG images and is related to the chiral structure of collagen. The is observed for collagen fibers tilted out of the image plane, and can have positive or negative values, revealing the relative polarity of collagen fibers within the tissue. The P‐SHG imaging was performed using a linear polarization‐in polarization‐out (PIPO) method on thin sections of pig tendon cut at different angles. The nonlinear chiral properties of collagen can be used to construct the 3D organization of collagen in the tissue and determine the orientation‐independent molecular susceptibility ratios of collagen fibers in the molecular frame of reference.      

Biological nascent evolution of snail bone and collagen revealed by nonlinear optical microscopy

Using nonlinear optical microscopy of coherent antistokes Raman scattering (CARS), second harmonic generation (SHG) and two‐photo excitation fluorescence, we in situ observed how the collagen and the bone grow synergistically and competitively during nascent biological evolution. The and ions were first observed to be dispersed in the liquid environment, and the collagen was observed 2 days later. With the help of the collagen, the and ions gradually moved closer to the collagen, and then the bone was produced in the forms of CaCO₃ and CaPO₃. When the bone was completed with the help of the collagen, the collagen gradually disappeared. The biological evolution of snail bone and collagen can be well revealed by CARS and SHG, and in addition, the biological evolution of structure and morphology can be clearly observed day by day.     

Estimating the distribution of heterogeneous treatment effects from treatment responses and from a predictive biomarker in a parallel-group RCT: A structural model approach

When the objective is to administer the best of two treatments to an individual, it is necessary to know his or her individual treatment effects (ITEs) and the correlation between the potential responses (PRs) and under treatments 1 and 0. Data that are generated in a parallel-group design RCT does not allow the ITE to be determined because only two samples from the marginal distributions of these PRs are observed and not the corresponding joint distribution. This is due to the “fundamental problem of causal inference.” Here, we present a counterfactual approach for estimating the joint distribution of two normally distributed responses to two treatments. This joint distribution of the PRs and can be estimated by assuming a bivariate normal distribution for the PRs and by using a normally distributed baseline biomarker functionally related to the sum . Such a functional relationship is plausible since a biomarker and the sum encode for the same information in an RCT, namely the variation between subjects. The estimation of the joint trivariate distribution is subjected to some constraints. These constraints can be framed in the context of linear regressions with regard to the proportions of variances in the responses explained and with regard to the residual variation. This presents new insights on the presence of treatment–biomarker interactions. We applied our approach to example data on exercise and heart rate and extended the approach to survival data.     

Intuitive and broadly applicable definitions of niche and fitness differences

Explaining nature’s biodiversity is a key challenge for science. To persist, populations must be able to grow faster when rare, a feature called negative frequency dependence and quantified as ‘niche differences’ () in modern coexistence theory. Here, we first show that available definitions of differ in how link to species interactions, are difficult to interpret and often apply to specific community types only. We then present a new definition of that is intuitive and applicable to a broader set of (modelled and empirical) communities than is currently the case, filling a main gap in the literature. Given , we also redefine fitness differences () and illustrate how and determine coexistence. Finally, we demonstrate how to apply our definitions to theoretical models and experimental data, and provide ideas on how they can facilitate comparison and synthesis in community ecology.     

Estimating the decision curve and its precision from three study designs

The decision curve plots the net benefit of a risk model for making decisions over a range of risk thresholds, corresponding to different ratios of misclassification costs. We discuss three methods to estimate the decision curve, together with corresponding methods of inference and methods to compare two risk models at a given risk threshold. One method uses risks (R) and a binary event indicator (Y) on the entire validation cohort. This method makes no assumptions on how well-calibrated the risk model is nor on the incidence of disease in the population and is comparatively robust to model miscalibration. If one assumes that the model is well-calibrated, one can compute a much more precise estimate of based on risks R alone. However, if the risk model is miscalibrated, serious bias can result. Case–control data can also be used to estimate if the incidence (or prevalence) of the event () is known. This strategy has comparable efficiency to using the full data, and its efficiency is only modestly less than that for the full data if the incidence is estimated from the mean of Y. We estimate variances using influence functions and propose a bootstrap procedure to obtain simultaneous confidence bands around the decision curve for a range of thresholds. The influence function approach to estimate variances can also be applied to cohorts derived from complex survey samples instead of simple random samples.     

Interferometric optical fiber sensor for optoacoustic endomicroscopy

Optical fiber sensors can offer robust and miniaturized detection of wideband ultrasound, yielding high sensitivity and immunity to electromagnetic interference. However, the lack of cost-effective manufacturing methods prevents the disseminated use of these sensors in biomedical applications. In this study, we developed and optimized a simple method to create optical cavities with high-quality mirrors for acoustic sensing based on micro-manipulation of UV-curable optical adhesives and electroless chemical silver deposition. This approach enables the manufacturing of ultrasound sensors based on Fabry-Pérot interferometers on optical fiber tips with minimal production costs. Characterization and high-resolution optoacoustic imaging experiments show that the manufacturing process yielded a fiber sensor with a small NEP () over a broad detection bandwidth (25 MHz), generally outperforming conventional piezoelectric based transducers. We discuss how the new manufacturing process leads to a high-performance acoustic detector that, due to low cost, can be used as a disposable sensor.     

Estimating occupancy of Chinese pangolin (Manis pentadactyla) in a protected and non‐protected area of Nepal

Chinese pangolin is the world's most heavily trafficked small mammal for luxury food and traditional medicine. Although their populations are declining worldwide, it is difficult to monitor their population status because of its rarity and nocturnal behavior. We used site occupancy (presence/absence) sampling of pangolin sign (i.e., active burrows) in a protected (Gaurishankar Conservation Area) and non‐protected area (Ramechhap District) of central Nepal with multiple environmental covariates to understand factors that may influence occupancy of Chinese pangolin. The average Chinese pangolin occupancy and detection probabilities were  ± SE = 0.77 ± 0.08;  ± SE = 0.27 ± 0.05, respectively. The detection probabilities of Chinese pangolin were higher in PA (  ± SE = 0.33 ± 0.03) than compared to non‐PA (  ± SE = 0.25 ± 0.04). The most important covariates for Chinese pangolin detectability were red soil (97%), food source (97.6%), distance to road (97.9%), and protected area (97%) and with respect to occupancy was elevation (97.9%). We recommended use of remote cameras and potentially GPS collar surveys to further investigate habitat use and site occupancy at regular intervals to provide more reliable conservation assessments.     

Mean growth rate when rare is not a reliable metric for persistence of species

The coexistence of many species within ecological communities poses a long‐standing theoretical puzzle. Modern coexistence theory (MCT) and related techniques explore this phenomenon by examining the chance of a species population growing from rarity in the presence of all other species. The mean growth rate when rare, , is used in MCT as a metric that measures persistence properties (like invasibility or time to extinction) of a population. Here we critique this reliance on and show that it fails to capture the effect of temporal random abundance variations on persistence properties. The problem becomes particularly severe when an increase in the amplitude of stochastic temporal environmental variations leads to an increase in , since at the same time it enhances random abundance fluctuations and the two effects are inherently intertwined. In this case, the chance of invasion and the mean extinction time of a population may even go down as increases.     

Determination of the diameter of simulated human capillaries using shifted position-diffuse reflectance imaging

Multiple diseases are associated with a wide spectrum of microvascular dysfunctions, microangiopathies and microcirculation disorders. Monitoring the microcirculation could thus be useful to diagnose many local and systemic circulatory disorders and to supervise critically ill patients. Many of the scores currently available to help identify the condition of a microcirculation disorder are invasive or leave scope for interpretation. Thus, the present study aims to investigate with Monte-Carlo simulations (as numerical solutions of the radiative transfer equation) whether shifted position-diffuse reflectance imaging (SP-DRI), a non-invasive diagnostic technique, reveals information on the capillary diameter to assess the state of the microcirculation. To quantify the SP-DRI signal, the modulation parameter K is introduced. It proves to correlate almost perfectly with the capillary diameter (), making it a valid parameter for reliably assessing microcirculation. SP-DRI is emerging as an important milestone on the way to early and conveniently diagnosing microcirculation associated diseases.     

Enhanced temporal and spatial resolution in super-resolution covariance imaging algorithm with deconvolution optimization

Based on the numerical analysis that covariance exhibits superior statistical precision than cumulant and variance, a new SOFI algorithm by calculating the n orders covariance for each pixel is presented with an almost -fold resolution improvement, which can be enhanced to 2ⁿ via deconvolution. An optimized deconvolution is also proposed by calculating the (n + 1) order SD associated with each n order covariance pixel, and introducing the results into the deconvolution as a damping factor to suppress noise generation. Moreover, a re-deconvolution of the covariance image with the covariance-equivalent point spread function is used to further increase the final resolution by above 2-fold. Simulated and experimental results show that this algorithm can significantly increase the temporal–spatial resolution of SOFI, meanwhile, preserve the sample's structure. Thus, a resolution of 58 nm is achieved for 20 experimental images, and the corresponding acquisition time is 0.8 seconds.     

Evidence for Depressed Reproduction of Golden Eagles in Washington

Beginning in 1977 the Washington Department of Fish and Wildlife conducted annual surveys to determine statewide golden eagle (Aquila chrysaetos) occupancy and productivity. Current interest in the regional and national status of the species prompted our investigation to determine utility of historical data in assessing trends in reproduction, and to test efficacy of a sampling protocol that surveyed randomly selected territories and also accounted for detection probability. We found evidence indicating poor reproduction from 38 annual surveys conducted at 301 known territories statewide between 1977 and 2014. At 256 territories in eastern Washington, USA, apparent occupancy was low ( = 50.9%) and nesting success declined by 22%. All reproductive parameters were higher than at 45 territories in western Washington. We tested efficacy of a sampling protocol in 2013 and 2014 by surveying 108 randomly selected eastern territories. Probability of detecting eagles for these years from ground (= 89%) was greater than from air (= 66%). Our estimate of territory occupancy, corrected by probability of detection, was lower in 2013 (= 56.7%, 95% CI = 46.3–66.7%) than in 2014 (= 73.7%, 95% CI = 64.8–81.7%), as was the estimated number of breeding pairs (2013: = 158, 95% CI = 151–164; 2014: = 187, 95% CI = 182–192). Higher productivity (young/occupied territory) in 2013 (= 0.59, 95% CI = 0.40–0.82) than in 2014 (= 0.41, 95% CI = 0.27–0.59) and lower proportions of ≥1 immature eagle among nesting pairs in 2013 (16%) than in 2014 (31%), suggested higher immature pairing among sampled pairs contributed to inter-year differences in these reproductive parameters. Current and historical evidence for depressed golden eagle nesting in Washington is consistent with documented effects from habitat conversion, prey declines, lead contamination, and wind power development. We recommend future surveys in eastern Washington adhere to the random sampling protocol and conduct surveys at regular intervals to allow for trend analysis of reproductive parameters to better monitor golden eagle status. Surveys in western Washington, conducted exclusively from ground at all nests, will improve detection and cost efficiency. © 2020 The Wildlife Society.     

Isotopically anomalous organic carbon in the aftermath of the Marinoan snowball Earth

Throughout most of the sedimentary record, the marine carbon cycle is interpreted as being in isotopic steady state. This is most commonly inferred via isotopic reconstructions, where two export fluxes (organic carbon and carbonate) are offset by a constant isotopic fractionation of ~25 (termed ). Sedimentary deposits immediately overlying the Marinoan snowball Earth diamictites, however, stray from this prediction. In stratigraphic sections from the Ol Formation (Mongolia) and Sheepbed Formation (Canada), we observe a temporary excursion where the organic matter has anomalously heavy C and is grossly decoupled from the carbonate C. This signal may reflect the unique biogeochemical conditions that persisted in the aftermath of snowball Earth. For example, physical oceanographic modeling suggests that a strong density gradient caused the ocean to remain stratified for about 50,000 years after termination of the Marinoan snowball event, during which time the surface ocean and continental weathering consumed the large atmospheric CO₂ reservoir. Further, we now better understand how C records of carbonate can be post‐depostionally altered and thus be misleading. In an attempt to explain the observed carbon isotope record, we developed a model that tracks the fluxes and isotopic values of carbon between the surface ocean, deep ocean, and atmosphere. By comparing the model output to the sedimentary data, stratification alone cannot generate the anomalous observed isotopic signal. Reproducing the heavy C in organic matter requires the progressively diminishing contribution of an additional anomalous source of organic matter. The exact source of this organic matter is unclear.     

Assessment of simulated osteoporosis in alveolar bone using optical coherence tomography

Steoporosis is a skeletal disorder that compromises bone resistance and its diagnosis is usually performed using dual energy X‐ray absorptiometry. Thus, the search for efficient diagnostic methods that do not involve the emission of ionizing radiation is necessary. This study proposed to use the Optical Coherence Tomography (OCT) to evaluate osteoporosis in alveolar bone. Osteoporosis lesions is simulated in vitro in porcine bones, and imaging is performed by OCT and micro‐computed tomography (micro‐CT). A developed algorithm is proposed to calculate the optical attenuation coefficient ( μ _t), mean optical attenuation coefficient (), integrated reflectivity (ΔR) and bone density ( BD). The , ΔR and BD parameters shows a good correlation to micro‐CT parameters (bone volume/tissue volume and total porosity). The μ _t and methods are negatively impacted by non‐uniform intensities distribution in osteoporosis images. In conclusion, BD and ΔR analysis demonstrates to be potential techniques for diagnosis and monitoring of osteoporosis using OCT.      

Another chemolithotrophic metabolism missing in nature: sulfur comproportionation

Chemotrophic microorganisms gain energy for cellular functions by catalyzing oxidation–reduction (redox) reactions that are out of equilibrium. Calculations of the Gibbs energy ( ΔG _r ) can identify whether a reaction is thermodynamically favourable and quantify the accompanying energy yield at the temperature, pressure and chemical composition in the system of interest. Based on carefully calculated values of ΔG _r , we predict a novel microbial metabolism – sulfur comproportionation (3H₂S + + 2H⁺ ⇌ 4S⁰ + 4H₂O). We show that at elevated concentrations of sulfide and sulfate in acidic environments over a broad temperature range, this putative metabolism can be exergonic ( ΔG _r <0), yielding ~30–50 kJ mol⁻¹. We suggest that this may be sufficient energy to support a chemolithotrophic metabolism currently missing from the literature. Other versions of this metabolism, comproportionation to thiosulfate (H₂S + ⇌ + H₂O) and to sulfite (H₂S + 3 ⇌ 4 + 2H⁺), are only moderately exergonic or endergonic even at ideal geochemical conditions. Natural and impacted environments, including sulfidic karst systems, shallow-sea hydrothermal vents, sites of acid mine drainage, and acid–sulfate crater lakes, may be ideal hunting grounds for finding microbial sulfur comproportionators.     

Anthropogenic Disturbance and Population Viability of Woodland Caribou in Ontario

One of the most challenging tasks in wildlife conservation and management is to clarify how spatial variation in land cover due to anthropogenic disturbance influences wildlife demography and long-term viability. To evaluate this, we compared rates of survival and population growth by woodland caribou (Rangifer tarandus caribou) from 2 study sites in northern Ontario, Canada that differed in the degree of anthropogenic disturbance because of commercial logging and road development, resulting in differences in predation risk due to gray wolves (Canis lupus). We used an individual-based model for population viability analysis (PVA) that incorporated adaptive patterns of caribou movement in relation to predation risk and food availability to predict stochastic variation in rates of caribou survival. Field estimates of annual survival rates for adult female caribou in the unlogged ( 0.90) and logged ( 0.76) study sites recorded during 2010–2014 did not differ significantly (P > 0.05) from values predicted by the individual-based PVA model (unlogged: = 0.87; logged: 0.79). Outcomes from the individual-based PVA model and a simpler stage-structured matrix model suggest that substantial differences in adult survival largely due to wolf predation are likely to lead to long-term decline of woodland caribou in the commercially logged landscape, whereas the unlogged landscape should be considerably more capable of sustaining caribou. Estimates of population growth rates (λ) for the 2010–2014 period differed little between the matrix model and the individual-based PVA model for the unlogged (matrix model = 1.01; individual-based model = 0.98) and logged landscape (matrix model = 0.88; individual-based model = 0.89). We applied the spatially explicit PVA model to assess the viability of woodland caribou across 14 woodland caribou ranges in Ontario. Outcomes of these simulations suggest that woodland caribou ranges that have experienced significant levels of commercial forestry activities in the past had annual growth rates <0.89, whereas caribou ranges that had not experienced commercial forestry operations had population growth rates >0.96. These differences were strongly related to regional variation in wolf densities. Our results suggest that increased wolf predation risk due to anthropogenic disturbance is of sufficient magnitude to cause appreciable risk of population decline in woodland caribou in Ontario. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.     

Important role of genetic drift in rapid polygenic adaptation

We analyzed a model to determine the factors that facilitate or limit rapid polygenic adaptation. This model includes population genetic terms of mutation and both directional and stabilizing selection on a highly polygenic trait in a diploid population of finite size. First, we derived the equilibrium distribution of the allele frequencies of the multilocus model by diffusion approximation. This formula describing the equilibrium allele frequencies as a mutation‐selection‐drift balance was examined by computer simulation using parameter values inferred for human height, a well‐studied polygenic trait. Second, assuming that a sudden environmental shift of the fitness optimum occurs while the population is in equilibrium, we analyzed the adaptation of the trait to the new optimum. The speed at which the trait mean approaches the new optimum increases with the equilibrium genetic variance. Thus, large population size and/or large mutation rate may facilitate rapid adaptation. Third, the contribution of an individual locus i to polygenic adaptation depends on the compound parameter , where is the effect size, the equilibrium frequency of the trait‐increasing allele of this locus, and . Thus, only loci with large values of this parameter contribute coherently to polygenic adaptation. Given that mutation rates are relatively small, this is more likely in large populations, in which the effects of drift are limited.     

Inverse problem approach to regularized regression models with application to predicting recovery after stroke

Regression modelling is a powerful statistical tool often used in biomedical and clinical research. It could be formulated as an inverse problem that measures the discrepancy between the target outcome and the data produced by representation of the modelled predictors. This approach could simultaneously perform variable selection and coefficient estimation. We focus particularly on a linear regression issue, , where is the parameter of interest and its components are the regression coefficients. The inverse problem finds an estimate for the parameter , which is mapped by the linear operator to the observed outcome data . This problem could be conveyed by finding a solution in the affine subspace . However, in the presence of collinearity, high-dimensional data and high conditioning number of the related covariance matrix, the solution may not be unique, so the introduction of prior information to reduce the subset and regularize the inverse problem is needed. Informed by Huber's robust statistics framework, we propose an optimal regularizer to the regression problem. We compare results of the proposed method and other penalized regression regularization methods: ridge, lasso, adaptive-lasso and elastic-net under different strong hypothesis such as high conditioning number of the covariance matrix and high error amplitude, on both simulated and real data from the South London Stroke Register. The proposed approach can be extended to mixed regression models. Our inverse problem framework coupled with robust statistics methodology offer new insights in statistical regression and learning. It could open a new research development for model fitting and learning.     

Optimal Strategies for Managing Wildlife Harvest Under System Change

Wildlife populations are experiencing shifting dynamics due to climate and landscape change. Management policies that fail to account for non-stationary dynamics may fail to achieve management objectives. We establish a framework for understanding optimal strategies for managing a theoretical harvested population under non-stationarity. Building from harvest theory, we develop scenarios representing changes in population growth rate () or carrying capacity () and derive time-dependent optimal harvest policies using stochastic dynamic programming. We then evaluate the cost of falsely assuming stationarity by comparing the outcomes of forward projections in which either the optimal policy or a stationary policy is applied. When declines over time, the stationary policy leads to an underharvest of the population, resulting in less harvest over the short term but leaving the population in a higher-value state. When declines over time, the stationary policy leads to overharvest, resulting in greater harvest returns in the short term but leaving the population in a lower and potentially more vulnerable state. This work demonstrates the basic properties of time-dependent harvest management and provides a framework for evaluating the many outstanding questions about optimal management strategies under climate change. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.