Modelling the impacts of ammonia emissions reductions on North American air quality

A unified regional air-quality modelling system (AURAMS) was used to investigate the effects of reductions in ammonia emissions on regional air quality, with a focus on particulate-matter formation. Three simulations of one-year duration were performed for a North American domain: (1) a base-case simulation using 2002 Canadian and US national emissions inventories augmented by a more detailed Canadian emissions inventory for agricultural ammonia; (2) a 30% North-American-wide reduction in agricultural ammonia emissions; and (3) a 50% reduction in Canadian beef-cattle ammonia emissions. The simulations show that a 30% continent-wide reduction in agricultural ammonia emissions lead to reductions in median hourly PM<sub>2.5</sub> mass of <1 μg m<sup>−3</sup> on an annual basis. The atmospheric response to these emission reductions displays marked seasonal variations, and on even shorter time scales, the impacts of the emissions reductions are highly episodic: 95th-percentile hourly PM<sub>2.5</sub> mass decreases can be up to a factor of six larger than the median values. <br><br> A key finding of the modelling work is the linkage between gas and aqueous chemistry and transport; reductions in ammonia emissions affect gaseous ammonia concentrations close to the emissions site, but substantial impacts on particulate matter and atmospheric deposition often occur at considerable distances downwind, with particle nitrate being the main vector of ammonia/um transport. Ammonia emissions reductions therefore have trans-boundary consequences downwind. Calculations of critical-load exceedances for sensitive ecosystems in Canada suggest that ammonia emission reductions will have a minimal impact on current ecosystem acidification within Canada, but may have a substantial impact on future ecosystem acidification. The 50% Canadian beef-cattle ammonia emissions reduction scenario was used to examine model sensitivity to uncertainties in the new Canadian agricultural ammonia emissions inventory, and the simulation results suggest that further work is needed to improve the emissions inventory for this particular sector. It should be noted that the model in its current form neglects coarse mode base cation chemistry, so the predicted effects of ammonia emissions reductions shown here should be considered upper limits

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Voicing quantification is more relevant than period perturbation in substitution voices: an advanced acoustical study

Quality of substitution voicing—i.e., phonation with a voice that is not generated by the vibration of two vocal folds—cannot be adequately evaluated with routinely used software for acoustic voice analysis that is aimed at ‘common’ dysphonias and nearly periodic voice signals. The AMPEX analysis program (Van Immerseel and Martens) has been shown previously to be able to detect periodicity in irregular signals with background noise, and to be suited for running speech. The validity of this analysis program is first tested using realistic synthesized voice signals with known levels of cycle-to-cycle perturbations and additive noise. Second, exhaustive acoustic analysis is performed of the voices of 116 patients surgically treated for advanced laryngeal cancer and recorded in seven European academic centers. All of them read out a short phonetically balanced passage. Patients were divided into six groups according to the oscillating structures they used to phonate. Results show that features related to quantification of voicing enable a distinction between the different groups, while the features reporting F0-instability fail to do so. Acoustic evaluation of voice quality in substitution voices thus best relies upon voicing quantification

War of Ontology Worlds: Mathematics, Computer Code, or Esperanto?

The use of structured knowledge representations—ontologies and terminologies—has become standard in biomedicine. Definitions of ontologies vary widely, as do the values and philosophies that underlie them. In seeking to make these views explicit, we conducted and summarized interviews with a dozen leading ontologists. Their views clustered into three broad perspectives that we summarize as mathematics, computer code, and Esperanto. Ontology as mathematics puts the ultimate premium on rigor and logic, symmetry and consistency of representation across scientific subfields, and the inclusion of only established, non-contradictory knowledge. Ontology as computer code focuses on utility and cultivates diversity, fitting ontologies to their purpose. Like computer languages C++, Prolog, and HTML, the code perspective holds that diverse applications warrant custom designed ontologies. Ontology as Esperanto focuses on facilitating cross-disciplinary communication, knowledge cross-referencing, and computation across datasets from diverse communities. We show how these views align with classical divides in science and suggest how a synthesis of their concerns could strengthen the next generation of biomedical ontologies

A Model for the Origin and Properties of Flicker-Induced Geometric Phosphenes

We present a model for flicker phosphenes, the spontaneous appearance of geometric patterns in the visual field when a subject is exposed to diffuse flickering light. We suggest that the phenomenon results from interaction of cortical lateral inhibition with resonant periodic stimuli. We find that the best temporal frequency for eliciting phosphenes is a multiple of intrinsic (damped) oscillatory rhythms in the cortex. We show how both the quantitative and qualitative aspects of the patterns change with frequency of stimulation and provide an explanation for these differences. We use Floquet theory combined with the theory of pattern formation to derive the parameter regimes where the phosphenes occur. We use symmetric bifurcation theory to show why low frequency flicker should produce hexagonal patterns while high frequency produces pinwheels, targets, and spirals

Features of Mild-to-Moderate COVID-19 Patients with Dysphonia

Introduction To explore the prevalence of dysphonia in European patients with mild-to-moderate COVID-19 and the clinical features of dysphonic patients. Methods The clinical and epidemiological data of 702 patients with mild-to-moderate COVID-19 were collected from 19 European Hospitals. The following data were extracted: age, sex, ethnicity, tobacco consumption, comorbidities, general and otolaryngological symptoms. Dysphonia and otolaryngological symptoms were self-assessed through a 4-point scale. The prevalence of dysphonia, as part of the COVID-19 symptoms, was assessed. The outcomes were compared between dysphonic and non-dysphonic patients. The association between dysphonia severity and outcomes was studied through Bayesian analysis. Results A total of 188 patients were dysphonic, accounting for 26.8% of cases. Females developed more frequently dysphonia than males (p=0.022). The proportion of smokers was significantly higher in the dysphonic group (p=0.042). The prevalence of the following symptoms was higher in dysphonic patients compared with non-dysphonic patients: cough, chest pain, sticky sputum, arthralgia, diarrhea, headache, fatigue, nausea and vomiting. The severity of dyspnea, dysphagia, ear pain, face pain, throat pain and nasal obstruction was higher in dysphonic group compared with non-dysphonic group. There were significant associations between the severity of dysphonia, dysphagia and cough. Conclusion Dysphonia may be encountered in a quarter of patients with mild-to-moderate COVID-19 and should be considered as a symptom list of the infection. Dysphonic COVID-19 patients are more symptomatic than non-dysphonic individuals. Future studies are needed to investigate the relevance of dysphonia in the COVID-19 clinical presentation

Gebiss: an ImageJ plugin for the specification of ground truth and the performance evaluation of 3D segmentation algorithms.

Background: Image segmentation is a crucial step in quantitative microscopy that helps to define regions of tissues, cells or subcellular compartments. Depending on the degree of user interactions, segmentation methods can be divided into manual, automated or semi-automated approaches. 3D image stacks usually require automated methods due to their large number of optical sections. However, certain applications benefit from manual or semi-automated approaches. Scenarios include the quantification of 3D images with poor signal-to-noise ratios or the generation of so-called ground truth segmentations that are used to evaluate the accuracy of automated segmentation methods. Results: We have developed Gebiss; an ImageJ plugin for the interactive segmentation, visualisation and quantification of 3D microscopic image stacks. We integrated a variety of existing plugins for threshold-based segmentation and volume visualisation. Conclusions: We demonstrate the application of Gebiss to the segmentation of nuclei in live Drosophila embryos and the quantification of neurodegeneration in Drosophila larval brains. Gebiss was developed as a cross-platform ImageJ plugin and is freely available on the web at http://imaging.bii.a-star.edu.sg/projects/gebiss

Disambiguating Multi–Modal Scene Representations Using Perceptual Grouping Constraints

In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that — in addition to commonly used geometric information — makes use of a novel multi–modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints