Pollination Ecology in the Southwest

Comparisons of the pollination biology of members of a number of genera (Prosopis, Helianthus, Opuntia, and Krameria) widespread in the arid American Southwest are made between sites in the Sonoran Desert of southern Arizona and the dry oak-juniper grasslands of central Texas. As in the majority of cases studied to date in the dry regions of the Southwest, solitary bees are the dominant pollinators in all of the systems examined. Rich arrays of oligolectic bees are associated with Prosopis, Helianthus, and Opuntia, but none with Krameria which offers oils rather than pollen and nectar as the primary floral reward. Nevertheless, Krameria appears to have the most restricted pollination system as none of the other taxa are obligately dependent on their specialist bees. Reward production and bee foraging activity were examined in Opuntia and Helianthus. In Helianthus, bimodal pollen presentation, but near constant nectar production, results in different activity patterns of the specialist and generalist bees visiting the flowers. Reward production is unimodal in the Opuntia species studied, but diurnal phenological differences can result in apparent partitioning of floral resources by foraging bees

Adaptive feedback analysis and control of programmable stimuli for assessment of cerebrovascular function

The assessment of cerebrovascular regulatory mechanisms often requires flexibly controlled and precisely timed changes in arterial blood pressure (ABP) and/or inspired CO2. In this study, a new system for inducing variations in mean ABP was designed, implemented and tested using programmable sequences and programmable controls to induce pressure changes through bilateral thigh cuffs. The system is also integrated with a computer-controlled switch to select air or a CO2/air mixture to be provided via a face mask. Adaptive feedback control of a pressure generator was required to meet stringent specifications for fast changes, and accuracy in timing and pressure levels applied by the thigh cuffs. The implemented system consists of a PC-based signal analysis/control unit, a pressure control unit and a CO2/air control unit. Initial evaluations were carried out to compare the cuff pressure control performances between adaptive and non-adaptive control configurations. Results show that the adaptive control method can reduce the mean error in sustaining target pressure by 99.57 % and reduce the transient time in pressure increases by 45.21 %. The system has proven a highly effective tool in ongoing research on brain blood flow control

Detection of impaired cerebral autoregulation improves by increasing arterial blood pressure variability

Although the assessment of dynamic cerebral autoregulation (CA) based on measurements of spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CBF) is a convenient and much used method, there remains uncertainty about its reliability. We tested the effects of increasing ABP variability, provoked by a modification of the thigh cuff method, on the ability of the autoregulation index to discriminate between normal and impaired CA, using hypercapnia as a surrogate for dynamic CA impairment. In 30 healthy volunteers, ABP (Finapres) and CBF velocity (CBFV, transcranial Doppler) were recorded at rest and during 5% CO(2) breathing, with and without pseudo-random sequence inflation and deflation of bilateral thigh cuffs. The application of thigh cuffs increased ABP and CBFV variabilities and was not associated with a distortion of the CBFV step response estimates for both normocapnic and hypercapnic conditions (P=0.59 and P=0.96, respectively). Sensitivity and specificity of CA impairment detection were improved with the thigh cuff method, with the area under the receiver-operator curve increasing from 0.746 to 0.859 (P=0.031). We conclude that the new method is a safe, efficient, and appealing alternative to currently existing assessment methods for the investigation of the status of CA

A Preliminary Examination of Variability Due to Build Location and Powder Feedstock in Additive Manufacture of Inconel 718 using Laser-Based Powder Bed Fusion

The production of metallic parts by additive manufacturing (AM) is of significant interest to industry, but in the absence of standards, practical design considerations for manufacturing engineers are not widely known. Within the context of powder bed fusion (PBF), many unknowns persist regarding variations in part quality due to part location on the build plate, process consistency, feedstock supplier, and machine manufacturer. In this paper, we investigate the mechanical property variance across the build platform and document the successful use of feedstock powders obtained from several suppliers for the manufacture of Inconel 718 tensile and Charpy specimens, built on an EOS M280 laser-based powder bed fusion system. Particular emphasis is placed on describing the manufacturing process design challenges encountered even for simple geometries. While many advocate that complexity is free when using AM, we find that AM can lead to expensive build failures given the current state of manufacturing process knowledge and that design for additive manufacture is required for successful application of AM techniques.Mechanical Engineerin

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting a generalized form of the governing reaction-diffusion partial differential equation (PDE). By allowing the diffusion and reaction terms to be multilayer perceptrons (MLPs), the nonlinear forms of these terms can be learned while simultaneously converging to the solution of the governing PDE. Further, the trained MLPs are used to guide the selection of biologically interpretable mechanistic forms of the PDE terms which provides new insights into the biological and physical mechanisms that govern the dynamics of the observed system. The method is evaluated on sparse real-world data from wound healing assays with varying initial cell densities [2]

Learning differential equation models from stochastic agent-based model simulations

Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology, and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel, and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread

Primary peripheral arterial stenoses and restenoses excised by transluminal atherectomy: A histopathologic study

Atherectomy is a new therapeutic intervention for the treatment of peripheral arterial disease, and permits the controlled excision and retrieval of portions of stenosing lesions. The gross and light microscopic features of 218 peripheral arterial stenoses resected from 100 patients by atherectomy were studied. One hundred seventy of these lesions were primary stenoses and 48 were restenoses subsequent to prior angioplasty or atherectomy. Microscopically, primary stenoses were composed of atherosclerotic plaque (150 lesions), fibrous intimai thickening (15 lesions) or thrombus alone (5 lesions). Atherosclerotic plaques had a variable morphology and, in one-third of cases, were accompanied by abundant surface thrombus that probably added to the severity of stenosis. Most patients with fibrous intimai thickening or thrombus alone had typical atherosclerotic plaque removed elsewhere from within the same artery.Intimai hyperplasia, with or without underlying residual plaque, was found at 36 sites of restenosis, the remaining 12 consisting of plaque only. Intimai hyperplasia had a distinctive histologic appearance and was due to smooth muscle cell proliferation within a loosely fibrous stroma. Superimposed thrombus may have contributed to arterial narrowing in 25% of hyperplastic and 8% of atherosclerotic restenoses (p = 0.41). Pathologic examination of tissues recovered by peripheral atherectomy is an important adjunct that may provide insight into the efficacy of vascular interventions and the phenomenon of postintervention restenosis

Evaluation of a weighting approach for performing sensitivity analysis after multiple imputation.

BACKGROUND: Multiple imputation (MI) is a well-recognised statistical technique for handling missing data. As usually implemented in standard statistical software, MI assumes that data are 'Missing at random' (MAR); an assumption that in many settings is implausible. It is not possible to distinguish whether data are MAR or 'Missing not at random' (MNAR) using the observed data, so it is desirable to discover the impact of departures from the MAR assumption on the MI results by conducting sensitivity analyses. A weighting approach based on a selection model has been proposed for performing MNAR analyses to assess the robustness of results obtained under standard MI to departures from MAR. METHODS: In this article, we use simulation to evaluate the weighting approach as a method for exploring possible departures from MAR, with missingness in a single variable, where the parameters of interest are the marginal mean (and probability) of a partially observed outcome variable and a measure of association between the outcome and a fully observed exposure. The simulation studies compare the weighting-based MNAR estimates for various numbers of imputations in small and large samples, for moderate to large magnitudes of departure from MAR, where the degree of departure from MAR was assumed known. Further, we evaluated a proposed graphical method, which uses the dataset with missing data, for obtaining a plausible range of values for the parameter that quantifies the magnitude of departure from MAR. RESULTS: Our simulation studies confirm that the weighting approach outperformed the MAR approach, but it still suffered from bias. In particular, our findings demonstrate that the weighting approach provides biased parameter estimates, even when a large number of imputations is performed. In the examples presented, the graphical approach for selecting a range of values for the possible departures from MAR did not capture the true parameter value of departure used in generating the data. CONCLUSIONS: Overall, the weighting approach is not recommended for sensitivity analyses following MI, and further research is required to develop more appropriate methods to perform such sensitivity analyses

Degenerate dispersive equations arising in the study of magma dynamics

An outstanding problem in Earth science is understanding the method of transport of magma in the Earth's mantle. Models for this process, transport in a viscously deformable porous media, give rise to scalar degenerate, dispersive, nonlinear wave equations. We establish a general local well-posedness for a physical class of data (roughly $H^1$) via fixed point methods. The strategy requires positive lower bounds on the solution. This is extended to global existence for a subset of possible nonlinearities by making use of certain conservation laws associated with the equations. Furthermore, we construct a Lyapunov energy functional, which is locally convex about the uniform state, and prove (global in time) nonlinear dynamic stability of the uniform state for any choice of nonlinearity. We compare the dynamics to that of other problems and discuss open questions concerning a larger range of nonlinearities, for which we conjecture global existence.Comment: 27 Pages, 7 figures are not present in this version. See http://www.columbia.edu/~grs2103/ for a PDF with figures. Submitted to Nonlinearit

Simple guide to starting a research group

Conducting cutting-edge research and scholarship becomes more complicated with each passing year; forming a collaborative research group offers a way to navigate this increasing complexity. Yet many individuals whose work might benefit from the formation of a collaborative team may feel overwhelmed by the prospect of attempting to build and maintain a research group. We propose this simple guide for starting and maintaining such an enterprise