Bio-inspired Attentive Segmentation of Retinal OCT Imaging

Albeit optical coherence imaging (OCT) is widely used to assess ophthalmic pathologies, localization of intra-retinal boundaries suffers from erroneous segmentations due to image artifacts or topological abnormalities. Although deep learning-based methods have been effectively applied in OCT imaging, accurate automated layer segmentation remains a challenging task, with the flexibility and precision of most methods being highly constrained. In this paper, we propose a novel method to segment all retinal layers, tailored to the bio-topological OCT geometry. In addition to traditional learning of shift-invariant features, our method learns in selected pixels horizontally and vertically, exploiting the orientation of the extracted features. In this way, the most discriminative retinal features are generated in a robust manner, while long-range pixel dependencies across spatial locations are efficiently captured. To validate the effectiveness and generalisation of our method, we implement three sets of networks based on different backbone models. Results on three independent studies show that our methodology consistently produces more accurate segmentations than state-of-the-art networks, and shows better precision and agreement with ground truth. Thus, our method not only improves segmentation, but also enhances the statistical power of clinical trials with layer thickness change outcomes

Techno–ecological synergies of solar energy for global sustainability

The strategic engineering of solar energy technologies—from individual rooftop modules to large solar energy power plants—can confer significant synergistic outcomes across industrial and ecological boundaries. Here, we propose techno–ecological synergy (TES), a framework for engineering mutually beneficial relationships between technological and ecological systems, as an approach to augment the sustainability of solar energy across a diverse suite of recipient environments, including land, food, water, and built-up systems. We provide a conceptual model and framework to describe 16 TESs of solar energy and characterize 20 potential techno–ecological synergistic outcomes of their use. For each solar energy TES, we also introduce metrics and illustrative assessments to demonstrate techno–ecological potential across multiple dimensions. The numerous applications of TES to solar energy technologies are unique among energy systems and represent a powerful frontier in sustainable engineering to minimize unintended consequences on nature associated with a rapid energy transition

Implications of the Plastid Genome Sequence of Typha (Typhaceae, Poales) for Understanding Genome Evolution in Poaceae

Plastid genomes of the grasses (Poaceae) are unusual in their organization and rates of sequence evolution. There has been a recent surge in the availability of grass plastid genome sequences, but a comprehensive comparative analysis of genome evolution has not been performed that includes any related families in the Poales. We report on the plastid genome of Typha latifolia, the first non-grass Poales sequenced to date, and we present comparisons of genome organization and sequence evolution within Poales. Our results confirm that grass plastid genomes exhibit acceleration in both genomic rearrangements and nucleotide substitutions. Poaceae have multiple structural rearrangements, including three inversions, three genes losses (accD, ycf1, ycf2), intron losses in two genes (clpP, rpoC1), and expansion of the inverted repeat (IR) into both large and small single-copy regions. These rearrangements are restricted to the Poaceae, and IR expansion into the small single-copy region correlates with the phylogeny of the family. Comparisons of 73 protein-coding genes for 47 angiosperms including nine Poaceae genera confirm that the branch leading to Poaceae has significantly accelerated rates of change relative to other monocots and angiosperms. Furthermore, rates of sequence evolution within grasses are lower, indicating a deceleration during diversification of the family. Overall there is a strong correlation between accelerated rates of genomic rearrangements and nucleotide substitutions in Poaceae, a phenomenon that has been noted recently throughout angiosperms. The cause of the correlation is unknown, but faulty DNA repair has been suggested in other systems including bacterial and animal mitochondrial genomes

Do indoor environments in schools influence student performance? A review of the literature

Limited research is available on potential adverse effects of school environments on academic performance, despite strong public concern. We examine the scientific evidence relevant to this relationship by reviewing available research relating schools and other indoor environments to human performance or attendance. As a primary focus, we critically review evidence for direct relationships between indoor environmental quality (IEQ) in buildings and performance or attendance. As a secondary focus, we summarize, without critique, evidence on potential connections indirectly linking IEQ to performance or attendance: relationships between IEQ and health, between health and performance or attendance, and between attendance and performance. The most persuasive direct evidence showed increases in indoor concentrations of nitrogen dioxide and outdoor concentrations of several specific pollutants to be related to reduced school attendance. The most persuasive indirect evidence showed indoor dampness and microbiologic pollutants to be related to asthma and respiratory infections, which have in turn been related to reduced performance and attendance. Furthermore, a substantial scientific literature links poor IEQ (e.g., low ventilation rate, excess moisture or formaldehyde) with respiratory and other health effects in children and adults. Overall, evidence suggests that poor IEQ in schools can influence the performance and attendance of students, primarily through health effects from indoor pollutants. Also, inadequate IEQ in schools seems sufficiently common to merit strong public concern. Evidence is available to justify (1) immediate actions to protect IEQ in schools and (2) focused research on exposures, prevention, and causation, to better guide policies and actions on IEQ in schools

Do indoor environments in schools influence student performance? A review of the literature

Limited research is available on potential adverse effects of school environments on academic performance, despite strong public concern. We examine the scientific evidence relevant to this relationship by reviewing available research relating schools and other indoor environments to human performance or attendance. As a primary focus, we critically review evidence for direct relationships between indoor environmental quality (IEQ) in buildings and performance or attendance. As a secondary focus, we summarize, without critique, evidence on potential connections indirectly linking IEQ to performance or attendance: relationships between IEQ and health, between health and performance or attendance, and between attendance and performance. The most persuasive direct evidence showed increases in indoor concentrations of nitrogen dioxide and outdoor concentrations of several specific pollutants to be related to reduced school attendance. The most persuasive indirect evidence showed indoor dampness and microbiologic pollutants to be related to asthma and respiratory infections, which have in turn been related to reduced performance and attendance. Furthermore, a substantial scientific literature links poor IEQ (e.g., low ventilation rate, excess moisture or formaldehyde) with respiratory and other health effects in children and adults. Overall, evidence suggests that poor IEQ in schools can influence the performance and attendance of students, primarily through health effects from indoor pollutants. Also, inadequate IEQ in schools seems sufficiently common to merit strong public concern. Evidence is available to justify (1) immediate actions to protect IEQ in schools and (2) focused research on exposures, prevention, and causation, to better guide policies and actions on IEQ in schools

Quantifying the Air Pollution Exposure Consequences of Distributed Electricity Generation

Private sector and governmental organizations have been promoting the deployment of small-scale, distributed electricity generation (DG) technologies for their many benefits as compared to the traditional paradigm of large, centralized power plants. While some researchers have investigated the impact of a shift toward DG in terms of energy use and even air pollutant concentrations, it is also important to evaluate the air pollutant exposure implications of this shift. We conducted a series of case studies within the state of California that combined air dispersion modeling and inhalation exposure assessment. Twenty-five central stations were selected and five air pollutant-emitting DG technologies were considered, including two that meet the 2003 and 2007 California Air Resources Board DG emissions standards (microturbines and fuel cells with on-site natural gas reformers, respectively). This investigation has revealed that the fraction of pollutant mass emitted that is inhaled by the downwind, exposed population can be more than an order of magnitude greater for all five DG technologies considered than for large, central-station power plants in California. This difference is a consequence mainly of the closer proximity of DG sources to densely populated areas as compared to typical central station, and is independent of the emissions characteristics of the plants assessed. Considering typical emission factors for the five DG technologies, the mass of pollutant inhaled per unit electricity delivered can be up to three orders of magnitude greater for DG units as compared to existing California central stations. To equalize the exposure burden between DG and central station technologies, DG emission factors will need to be reduced to a range between the level of the cleanest, new central stations in California and an order of magnitude below those levels, depending on the pollutant and siting. We conclude that there is reason to caution against an unmitigated embrace of DG technologies that emit air pollutants so that they do not pose a greater public health burden than the current electricity generation system

Methane Leaks from Natural Gas Systems Follow Extreme Distributions

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH₄) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ∼15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH₄ emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies

Methane Leaks from Natural Gas Systems Follow Extreme Distributions

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH₄) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ∼15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH₄ emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies