Ecodriving and Carbon Footprinting: Understanding How Public Education Can Reduce Greenhouse Gas Emissions and Fuel Use

Ecodriving is a collection of changes to driving behavior and vehicle maintenance designed to impact fuel consumption and greenhouse gas (GHG) emissions in existing vehicles. Because of its promise to improve fuel economy within the existing fleet, ecodriving has gained increased attention in North America. One strategy to improve ecodriving is through public education with information on how to ecodrive. This report provides a review and study of ecodriving from several angles. The report offers a literature review of previous work and programs in ecodriving across the world. In addition, researchers completed interviews with experts in the field of public relations and public message campaigns to ascertain best practices for public campaigns. Further, the study also completed a set of focus groups evaluating consumer response to a series of websites that displayed ecodriving information. Finally, researchers conducted a set of surveys, including a controlled stated-response study conducted with approximately 100 University of California, Berkeley faculty, staff, and students, assessing the effectiveness of static ecodriving web-based information as well as an intercept clipboard survey in the San Francisco Bay Area. The stated-response study consisted of a comparison of the experimental and control groups. It found that exposure to ecodriving information influenced people’s driving behavior and some maintenance practices. The experimental group’s distributional shift was statistically significant, particularly for key practices including: lower highway cruising speed, driving behavior adjustment, and proper tire inflation. Within the experimental group (N = 51), fewer respondents significantly changed their maintenance practices (16%) than the majority that altered some driving practices (71%). This suggests intentionally altering driving behavior is easier than planning better maintenance practices. While it was evident that not everyone modifies their behavior as a result of reviewing the ecodriving website, even small shifts in behavior due to inexpensive information dissemination could be deemed cost effective in reducing fuel consumption and emissions

Protein refractive index increment is determined by conformation as well as composition.

The refractive index gradient of the eye lens is controlled by the concentration and distribution of its component crystallin proteins, which are highly enriched in polarizable amino acids. The current understanding of the refractive index increment ([Formula: see text]) of proteins is described using an additive model wherein the refractivity and specific volume of each amino acid type contributes according to abundance in the primary sequence. Here we present experimental measurements of [Formula: see text] for crystallins from the human lens and those of aquatic animals under uniform solvent conditions. In all cases, the measured values are much higher than those predicted from primary sequence alone, suggesting that structural factors also contribute to protein refractive index

The Weak Clustering of Gas-Rich Galaxies

We examine the clustering properties of HI-selected galaxies through an analysis of the HI Parkes All-Sky Survey Catalogue (HICAT) two-point correlation function. Various sub-samples are extracted from this catalogue to study the overall clustering of HI-rich galaxies and its dependence on luminosity, HI gas mass and rotational velocity. These samples cover the entire southern sky Dec < 0 deg, containing up to 4,174 galaxies over the radial velocity range 300-12,700 km/s. A scale length of r_0 = 3.45 +/- 0.25 Mpc/h and slope of gamma = 1.47 +/- 0.08 is obtained for the HI-rich galaxy real-space correlation function, making gas-rich galaxies among the most weakly clustered objects known. HI-selected galaxies also exhibit weaker clustering than optically selected galaxies of comparable luminosities. Good agreement is found between our results and those of synthetic HI-rich galaxy catalogues generated from the Millennium Run CDM simulation. Bisecting HICAT using different parameter cuts, clustering is found to depend most strongly on rotational velocity and luminosity, while the dependency on HI mass is marginal. Splitting the sample around v_rot = 108 km/s, a scale length of r_0 = 2.86 +/- 0.46 Mpc/h is found for galaxies with low rotational velocities compared to r_0 = 3.96 +/- 0.33 Mpc/h for the high rotational velocity sample.Comment: Accepted for publication in the Astrophysical Journa

Cathodoluminescence hyperspectral imaging of trench-like defects in InGaN/GaN quantum well structures

Optoelectronic devices based on the III-nitride system exhibit remarkably good optical efficiencies despite suffering from a large density of defects. In this work we use cathodoluminescence (CL) hyperspectral imaging to study InGaN/GaN multiple quantum well (MQW) structures. Different types of trench defects with varying trench width, namely wide or narrow trenches forming closed loops and open loops, are investigated in the same hyperspectral CL measurement. A strong redshift (90 meV) and intensity increase of the MQW emission is demonstrated for regions enclosed by wide trenches, whereas those within narrower trenches only exhibit a small redshift (10 meV) and a slight reduction of intensity compared with the defect-free surrounding area. Transmission electron microscopy (TEM) showed that some trench defects consist of a raised central area, which is caused by an increase of about 40% in the thickness of the InGaN wells. The causes of the changes in luminescences are also discussed in relation to TEM results identifying the underlying structure of the defect. Understanding these defects and their emission characteristics is important for further enhancement and development of light-emitting diodes

Network Hamiltonian models reveal pathways to amyloid fibril formation

Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer’s disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies remain elusive. One contributing factor to the lack of progress on this challenging problem is incomplete understanding of the mechanisms by which these locally ordered protein aggregates self-assemble in solution. Many current models of amyloid deposition diseases posit that the most toxic species are oligomers that form either along the pathway to forming fibrils or in competition with their formation, making it even more critical to understand the kinetics of fibrillization. A recently introduced topological model for aggregation based on network Hamiltonians is capable of recapitulating the entire process of amyloid fibril formation, beginning with thousands of free monomers and ending with kinetically accessible and thermodynamically stable amyloid fibril structures. The model can be parameterized to match the five topological classes encompassing all amyloid fibril structures so far discovered in the PDB. This paper introduces a set of network statistical and topological metrics for quantitative analysis and characterization of the fibrillization mechanisms predicted by the network Hamiltonian model. The results not only provide insight into different mechanisms leading to similar fibril structures, but also offer targets for future experimental exploration into the mechanisms by which fibrils form

Differential expression of microRNA-206 and its target genes in pre-eclampsia

Objectives: Pre-eclampsia is a multi-system disease that significantly contributes to maternal and fetal morbidity and mortality. In this study, we used a non-biased microarray approach to identify novel circulating miRNAs in maternal plasma that may be associated with pre-eclampsia. Methods: Plasma samples were obtained at 16 and 28 weeks of gestation from 18 women who later developed pre-eclampsia (cases) and 18 matched women with normotensive pregnancies (controls). We studied miRNA expression profiles in plasma and subsequently confirmed miRNA and target gene expression in placenta samples. Placental samples were obtained from an independent cohort of 19 women with pre-eclampsia matched with 19 women with normotensive pregnancies. Results: From the microarray, we identified 1 miRNA that was significantly differentially expressed between cases and controls at 16 weeks of gestation and 6 miRNAs that were significantly differentially expressed at 28 weeks. Following qPCR validation only one, miR-206, was found to be significantly increased in 28 week samples in women who later developed pre-eclampsia (1.4 fold change ± 0.2). The trend for increase in miR-206 expression was mirrored within placental tissue from women with pre-eclampsia. In parallel, IGF-1, a target gene of miR-206, was also found to be down-regulated (0.41 ± 0.04) in placental tissue from women with pre-eclampsia. miR-206 expression was also detectable in myometrium tissue and trophoblast cell lines. Conclusions: Our pilot study has identified miRNA-206 as a novel factor up-regulated in pre-eclampsia within the maternal circulation and in placental tissue

Precipitation regime change in Western North America: The role of Atmospheric Rivers.

Daily precipitation in California has been projected to become less frequent even as precipitation extremes intensify, leading to uncertainty in the overall response to climate warming. Precipitation extremes are historically associated with Atmospheric Rivers (ARs). Sixteen global climate models are evaluated for realism in modeled historical AR behavior and contribution of the resulting daily precipitation to annual total precipitation over Western North America. The five most realistic models display consistent changes in future AR behavior, constraining the spread of the full ensemble. They, moreover, project increasing year-to-year variability of total annual precipitation, particularly over California, where change in total annual precipitation is not projected with confidence. Focusing on three representative river basins along the West Coast, we show that, while the decrease in precipitation frequency is mostly due to non-AR events, the increase in heavy and extreme precipitation is almost entirely due to ARs. This research demonstrates that examining meteorological causes of precipitation regime change can lead to better and more nuanced understanding of climate projections. It highlights the critical role of future changes in ARs to Western water resources, especially over California

Neural upscaling from residue-level protein structure networks to atomistic structures

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly com-pressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 µs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs