Dysfunctional cGMP Signaling Leads to Age-Related Retinal Vascular Alterations and Astrocyte Remodeling in Mice

The nitric oxide–guanylyl cyclase-1–cyclic guanylate monophosphate (NO–GC-1–cGMP) pathway is integral to the control of vascular tone and morphology. Mice lacking the alpha catalytic domain of guanylate cyclase (GC1^−/−) develop retinal ganglion cell (RGC) degeneration with age, with only modest fluctuations in intraocular pressure (IOP). Increasing the bioavailability of cGMP in GC1^−/− mice prevents neurodegeneration independently of IOP, suggesting alternative mechanisms of retinal neurodegeneration. In continuation to these studies, we explored the hypothesis that dysfunctional cGMP signaling leads to changes in the neurovascular unit that may contribute to RGC degeneration. We assessed retinal vasculature and astrocyte morphology in young and aged GC1^−/− and wild type mice. GC1^−/− mice exhibit increased peripheral retinal vessel dilation and shorter retinal vessel branching with increasing age compared to Wt mice. Astrocyte cell morphology is aberrant, and glial fibrillary acidic protein (GFAP) density is increased in young and aged GC1^−/− mice, with areas of dense astrocyte matting around blood vessels. Our results suggest that proper cGMP signaling is essential to retinal vessel morphology with increasing age. Vascular changed are preceded by alterations in astrocyte morphology which may together contribute to retinal neurodegeneration and loss of visual acuity observed in GC1^−/− mice.     

The effect of microstructure on the permeability of metallic foams

Pressure drop was measured across complex and simple structure metallic foams at different velocity ranges using air as working fluid. Darcian and non-Darcian permeability parameters, K and C, were determined by fitting experimental data with widely accepted quadratic model of Hazen-Dupuit-Darcy. Generally, the experimental results are in good agreement with the model. The differences in K and C values between the two types of metallic foams are due to the different microstructure. For the simple structure specimens, permeability K increased whereas non-Darcian permeability C decreased with increasing pore diameter. The effect of pore size on the permeability of complex structure metallic foams seems to be opposite to that observed with the simple structure specimens and to results reported by other researchers on other porous medium. This discrepancy mainly stems from the differences in window concentration in addition to some heterogeneity in the foam that impeded the gas flow on one side of the specimens. The difference in pressure drop observed in the different metallic foams is due to combined effect of K and C. However, for simple structure foams, K and C could be predicted by Ergun-like model using appropriate values for the empirical constants. The permeability K is significantly affected by pore size and porosity. The quadratic term of Hazen-Dupuit-Darcy equation is mainly due to the inertia of the flow and partially to the drag exerted by the microstructure of the metallic foam. For both foams, as the porosity increases, pressure drop decreases and permeability, K, increases. The introduction of the open cross sectional area term enabled better understanding of the permeability of metallic foams with intricate morphologies.     

The link between built environment, pedestrian activity and pedestrian-vehicle collision occurrence at signalized intersections

This paper studies the influence of built environment (BE) – including land use types, road network connectivity, transit supply and demographic characteristics – on pedestrian activity and pedestrian–vehicle collision occurrence. For this purpose, a two-equation modeling framework is proposed to investigate the effect of built environment on both pedestrian activity and vehicle–pedestrian collision frequency at signalized intersections. Using accident data of ambulance services in the City of Montreal, the applicability of our framework is illustrated. Different model settings were attempted as part of a model sensitivity analysis. Among other results, it was found that the BE in the proximity of an intersection has a powerful association with pedestrian activity but a small direct effect on pedestrian–vehicle collision frequency. This suggests that the impact of BE is mainly mediated through pedestrian activity. In other words, strategies that encourage densification, mix of land uses and increase in transit supply will increase pedestrian activity and may indirectly, with no supplementary safety strategies, increase the total number of injured pedestrians. In accordance with previous research, the number of motor vehicles entering a particular intersection is the main determinant of collision frequency. Our results show that a 30% reduction in the traffic volume would reduce the total number of injured pedestrians by 35% and the average risk of pedestrian collision by 50% at the intersections under analysis. Major arterials are found to have a double negative effect on pedestrian safety. They are positively linked to traffic but negatively associated with pedestrian activity. The proposed framework is useful for the identification of effective pedestrian safety actions, the prediction of pedestrian volumes and the appropriate safety design of new urban developments that encourage walking.     

Copolymerization of elemental sulfur with styrene

Copolymerization of elemental sulfur with styrene in the presence and absence of metallic sodium was studied at 120°C and 138°C. Propagation of the reaction was followed by gel permeation chromatography (GPC). Glass transition temperatures of all samples were obtained by differential scanning calorimetry (DSC). Reaction products were fractionated with a preparative-type GPC, and each fraction was characterized by DSC, vapor pressure osmometry, infrared spectrophotometry, and both proton and carbon-13 nuclear magnetic resonance spectrometry. Results indicate that the product is a true copolymer of styrene and sulfur. Kinetics of the copolymerization were studied using GPC to monitor styrene and sulfur concentrations. The initial rate of copolymerization (as followed by the consumption of styrene and sulfur) decreases with increasing initial styrene to sulfur ratio. From kinetic analyses, ratios of the rate constants of homo- and copolymerization were determined. Copolymerization of the reactants is more spontaneous than homopolymerization. The reactivity ratios obtained are 0.2 for styrene and 0.6 for sulfur.     

Evolution of porosity, shrinkage and density of pasta fortified with pea protein concentrate during drying

The aim of this work was to study the impact of fortification with commercial pea protein concentrate on the evolution of the moisture content, density, shrinkage and porosity of pasta made from durum wheat semolina during drying. Pasta were processed from durum wheat semolina enriched with pea protein concentrate at 0, 5, 10 and 15 g 100-g-dry matter⁻¹ and dried at low (40 °C) and high (80 °C) temperature. Moisture content, density, shrinkage and porosity and effective moisture diffusivity coefficients were linked through theoretical development. It enabled to study the behaviour of the properties as a function of drying time. The results showed that drying temperature has a greater effect on the studied properties than enrichment with pea protein concentrate. Drying at 80 °C increased radial and total shrinkage compared to drying at 40 °C, but no differences were observed for longitudinal shrinkage. Pasta dried at 80 °C were denser and overall less porous, but had greater internal porosity. The volumetric percentage of water lost during drying replaced by air within the pasta matrix was lower at 80 °C. Scanning electron microscopy analysis showed that the gluten network of pasta dried at 80 °C seems denser and more continuous. Effective moisture diffusivity coefficients of pasta dried at 80 °C were higher at 5 and 10 g 100-g-dry matter⁻¹ enrichment level compared to the control.     

Cyclist activity and injury risk analysis at signalized intersections: A Bayesian modelling approach

This study proposes a two-equation Bayesian modelling approach to simultaneously study cyclist injury occurrence and bicycle activity at signalized intersections as joint outcomes. This approach deals with the potential presence of endogeneity and unobserved heterogeneities and is used to identify factors associated with both cyclist injuries and volumes. Its application to identify high-risk corridors is also illustrated. Montreal, Quebec, Canada is the application environment, using an extensive inventory of a large sample of signalized intersections containing disaggregate motor-vehicle traffic volumes and bicycle flows, geometric design, traffic control and built environment characteristics in the vicinity of the intersections. Cyclist injury data for the period of 2003–2008 is used in this study. Also, manual bicycle counts were standardized using temporal and weather adjustment factors to obtain average annual daily volumes. Results confirm and quantify the effects of both bicycle and motor-vehicle flows on cyclist injury occurrence. Accordingly, more cyclists at an intersection translate into more cyclist injuries but lower injury rates due to the non-linear association between bicycle volume and injury occurrence. Furthermore, the results emphasize the importance of turning motor-vehicle movements. The presence of bus stops and total crosswalk length increase cyclist injury occurrence whereas the presence of a raised median has the opposite effect. Bicycle activity through intersections was found to increase as employment, number of metro stations, land use mix, area of commercial land use type, length of bicycle facilities and the presence of schools within 50–800 m of the intersection increase. Intersections with three approaches are expected to have fewer cyclists than those with four. Using Bayesian analysis, expected injury frequency and injury rates were estimated for each intersection and used to rank corridors. Corridors with high bicycle volumes, located mainly in the central neighbourhoods of Montreal, have lower risk of injury. These results may reflect the “safety in numbers” hypothesis or cyclist preference towards safer intersections and corridors. Despite these corridors having a lower individual risk, they are nevertheless associated with a greater number of injuries.     

Multimodal injury risk analysis of road users at signalized and non-signalized intersections

This paper proposes a multimodal approach to study safety at intersections by simultaneously analysing the safety and flow outcomes for both motorized and non-motorized traffic. This study uses an extensive inventory of signalized and non-signalized intersections on the island of Montreal, Quebec, Canada, containing disaggregate motor-vehicle, cyclist and pedestrian flows, injury data, geometric design, traffic control and built environment characteristics in the vicinity of each intersection. Bayesian multivariate Poisson models are used to analyze the injury and traffic flow outcomes and to develop safety performance functions for each mode at both facilities. After model calibration, contributing injury frequency factors are identified. Injury frequency and injury risk measures are then generated to carry out a comparative study to identify which mode is at greatest risk at intersections in Montreal. Among other results, this study identified the significant effect that motor-vehicle traffic imposes on cyclist and pedestrian injury occurrence. Motor-vehicle traffic is the main risk determinant for all injury and intersection types. This highlights the need for safety improvements for cyclists and pedestrians who are, on average, at 14 and12 times greater risk than motorists, respectively, at signalized intersections. Aside from exposure measures, this work also identifies some geometric design and built environment characteristics affecting injury occurrence for cyclists, pedestrians and motor-vehicle occupants.     

A probabilistic multimodal approach for predicting listener backchannels

During face-to-face interactions, listeners use backchannel feedback such as head nods as a signal to the speaker that the communication is working and that they should continue speaking. Predicting these backchannel opportunities is an important milestone for building engaging and natural virtual humans. In this paper we show how sequential probabilistic models (e.g., Hidden Markov Model or Conditional Random Fields) can automatically learn from a database of human-to-human interactions to predict listener backchannels using the speaker multimodal output features (e.g., prosody, spoken words and eye gaze). The main challenges addressed in this paper are automatic selection of the relevant features and optimal feature representation for probabilistic models. For prediction of visual backchannel cues (i.e., head nods), our prediction model shows a statistically significant improvement over a previously published approach based on hand-crafted rules.