Problem: The prospect that urban heat island (UHI) effects and climate change may increase urban temperatures is a problem for cities that actively promote urban redevelopment and higher densities. One possible UHI mitigation strategy is to plant more trees and other irrigated vegetation to prevent daytime heat storage and facilitate nighttime cooling, but this requires water resources that are limited in a desert city like Phoenix.

Purpose: We investigated the tradeoffs between water use and nighttime cooling inherent in urban form and land use choices.

Methods: We used a Local-Scale Urban Meteorological Parameterization Scheme (LUMPS) model to examine the variation in temperature and evaporation in 10 census tracts in Phoenix's urban core. After validating results with estimates of outdoor water use based on tract-level city water records and satellite imagery, we used the model to simulate the temperature and water use consequences of implementing three different scenarios.

Results and conclusions: We found that increasing irrigated landscaping lowers nighttime temperatures, but this relationship is not linear; the greatest reductions occur in the least vegetated neighborhoods. A ratio of the change in water use to temperature impact reached a threshold beyond which increased outdoor water use did little to ameliorate UHI effects.

Takeaway for practice: There is no one design and landscape plan capable of addressing increasing UHI and climate effects everywhere. Any one strategy will have inconsistent results if applied across all urban landscape features and may lead to an inefficient allocation of scarce water resources.

Research Support: This work was supported by the National Science Foundation (NSF) under Grant SES-0345945 (Decision Center for a Desert City) and by the City of Phoenix Water Services Department. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.

This study addresses a classic sustainability challenge—the tradeoff between water conservation and temperature amelioration in rapidly growing cities, using Phoenix, Arizona and Portland, Oregon as case studies. An urban energy balance model— LUMPS (Local-Scale Urban Meteorological Parameterization Scheme)—is used to represent the tradeoff between outdoor water use and nighttime cooling during hot, dry summer months. Tradeoffs were characterized under three scenarios of land use change and three climate-change assumptions. Decreasing vegetation density reduced outdoor water use but sacrificed nighttime cooling. Increasing vegetated surfaces accelerated nighttime cooling, but increased outdoor water use by ~20%. Replacing impervious surfaces with buildings achieved similar improvements in nighttime cooling with minimal increases in outdoor water use; it was the most water-efficient cooling strategy. The fact that nighttime cooling rates and outdoor water use were more sensitive to land use scenarios than climate-change simulations suggested that cities can adapt to a warmer climate by manipulating land use.

The human gut microbiome is a complex community of microorganisms. These microbes play an important role in host health by contributing essential compounds and acting as a barrier against pathogens. However, these communities and associated functions can be impacted by factors like disease and diet. In particular, microbial fermentation of dietary components like polysaccharides, proteins, and fats that reach the gut are being examined to better understand how these biopolymers are utilized and affect community structure. Thus, evaluating the accuracy of methods used to quantify specific macromolecules is crucial to gaining a precise understanding of how gut microbes hydrolyze those substrates. This study presents findings on the accuracy of the Megazyme RS kit (Rapid) modified for high performance liquid chromatography (HPLC) readings and the DC Protein Assay when performed on samples from complex gut media with potato starch treatments and bovine serum albumin (BSA) treatments. Overall, our data indicates that the megazyme RS kit needs further modification to detect expected starch content with the HPLC and that the DC Protein Assay is not suitable for specific protein analysis.

Recent studies suggest a role for the microbiota in autism spectrum disorders (ASD), potentially arising from their role in modulating the immune system and gastrointestinal (GI) function or from gut–brain interactions dependent or independent from the immune system. GI problems such as chronic constipation and/or diarrhea are common in children with ASD, and significantly worsen their behavior and their quality of life. Here we first summarize previously published data supporting that GI dysfunction is common in individuals with ASD and the role of the microbiota in ASD. Second, by comparing with other publically available microbiome datasets, we provide some evidence that the shifted microbiota can be a result of westernization and that this shift could also be framing an altered immune system. Third, we explore the possibility that gut–brain interactions could also be a direct result of microbially produced metabolites.

There is a growing body of scientific evidence that the health of the microbiome (the trillions of microbes that inhabit the human host) plays an important role in maintaining the health of the host and that disruptions in the microbiome may play a role in certain disease processes. An increasing number of research studies have provided evidence that the composition of the gut (enteric) microbiome (GM) in at least a subset of individuals with autism spectrum disorder (ASD) deviates from what is usually observed in typically developing individuals. There are several lines of research that suggest that specific changes in the GM could be causative or highly associated with driving core and associated ASD symptoms, pathology, and comorbidities which include gastrointestinal symptoms, although it is also a possibility that these changes, in whole or in part, could be a consequence of underlying pathophysiological features associated with ASD. However, if the GM truly plays a causative role in ASD, then the manipulation of the GM could potentially be leveraged as a therapeutic approach to improve ASD symptoms and/or comorbidities, including gastrointestinal symptoms.

One approach to investigating this possibility in greater detail includes a highly controlled clinical trial in which the GM is systematically manipulated to determine its significance in individuals with ASD. To outline the important issues that would be required to design such a study, a group of clinicians, research scientists, and parents of children with ASD participated in an interdisciplinary daylong workshop as an extension of the 1st International Symposium on the Microbiome in Health and Disease with a Special Focus on Autism (www.microbiome-autism.com). The group considered several aspects of designing clinical studies, including clinical trial design, treatments that could potentially be used in a clinical trial, appropriate ASD participants for the clinical trial, behavioral and cognitive assessments, important biomarkers, safety concerns, and ethical considerations. Overall, the group not only felt that this was a promising area of research for the ASD population and a promising avenue for potential treatment but also felt that further basic and translational research was needed to clarify the clinical utility of such treatments and to elucidate possible mechanisms responsible for a clinical response, so that new treatments and approaches may be discovered and/or fostered in the future.

Critical flicker fusion thresholds (CFFTs) describe when quick amplitude modulations of a light source become undetectable as the frequency of the modulation increases and are thought to underlie a number of visual processing skills, including reading. Here, we compare the impact of two vision-training approaches, one involving contrast sensitivity training and the other directional dot-motion training, compared to an active control group trained on Sudoku. The three training paradigms were compared on their effectiveness for altering CFFT. Directional dot-motion and contrast sensitivity training resulted in significant improvement in CFFT, while the Sudoku group did not yield significant improvement. This finding indicates that dot-motion and contrast sensitivity training similarly transfer to effect changes in CFFT. The results, combined with prior research linking CFFT to high-order cognitive processes such as reading ability, and studies showing positive impact of both dot-motion and contrast sensitivity training in reading, provide a possible mechanistic link of how these different training approaches impact reading abilities.

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.