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Drivers with neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are at increased risk of experiencing driving difficulties. An important aspect of driving safety and skill involves hazard detection. This functional magnetic resonance imaging study examined the neural responses associated with driving hazard detection in drivers with ASD, ADHD, and typically developing (TD) drivers. Forty participants (12 ASD, 15 ADHD, 13 TD) ages 16-30 years completed a driving simulator task in which they encountered social and nonsocial hazards; reaction time (RT) for responding to hazards was measured. Participants then completed a similar hazard detection task in the MRI scanner so that neural response to hazards could be measured. Activation of regions of interest considered part of the executive function (EF) and theory of mind (ToM) networks were examined and related to driving simulator behavior. Results showed that stronger activation of the EF network during social hazard processing, including the bilateral dorsolateral prefrontal cortex and posterior parietal cortex, was associated with faster RT to social hazards among drivers with ADHD, but not among drivers with ASD. This provides the first evidence of a relationship between EF network brain activation and driving skills in ADHD and suggests that alterations in this network may underlie driving behavior. In comparison, the current study did not observe a relationship between ToM network activation and RT to social hazards in any group. This study lays the groundwork for relating neural activation to driving behavior among individuals with NDDs.

Individuals with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) may exhibit driving difficulties due to cognitive impairments such as time perception difficulties, a construct related to the perception of time-to-collision (TTC). This study examined the timing abilities of drivers with ASD and ADHD. Sixty participants (nADHD = 20, nASD = 20, nTD = 20) completed a time reproduction task and a TTC estimation task in a driving simulator. Results indicated drivers with ASD were less precise in time reproduction across all time intervals and over-reproduced time at shorter intervals. Drivers with ASD produced larger TTC estimates when driving at a faster speed compared to typically developing drivers. Drivers with ASD, but not ADHD, appear to present difficulties in time estimation abilities.

Stratospheric aerosol geoengineering has been proposed as a potential solution to reduce climate change and its impacts. Here, we explore the responses of the Hadley circulation (HC) intensity and the intertropical convergence zone (ITCZ) using the strategic stratospheric aerosol geoengineering, in which sulfur dioxide was injected into the stratosphere at four different locations to maintain the global-mean surface temperature and the interhemispheric and equator-to-pole temperature gradients at present-day values (baseline). Simulations show that, relative to the baseline, strategic stratospheric aerosol geoengineering generally maintains northern winter December–January–February (DJF) HC intensity under RCP8.5, while it overcompensates for the greenhouse gas (GHG)-forced southern winter June–July–August (JJA) HC intensity increase, producing a 3.5 ± 0.4% weakening. The residual change of southern HC intensity in JJA is mainly associated with stratospheric heating and tropospheric temperature response due to enhanced stratospheric aerosol concentrations. Geoengineering overcompensates for the GHG-driven northward ITCZ shifts, producing 0.7° ± 0.1° and 0.2° ± 0.1° latitude southward migrations in JJA and DJF, respectively relative to the baseline. These migrations are affected by tropical interhemispheric temperature differences both at the surface and in the free troposphere. Further strategies for reducing the residual change of HC intensity and ITCZ shifts under stratospheric aerosol geoengineering could involve minimizing stratospheric heating and restoring and preserving the present-day tropical tropospheric interhemispheric temperature differences.

Background Little is known about the preferred treatment strategies of chiropractors in managing low back pain patients with prior lumbar fusions. There are several case reports which describe chiropractic care following surgical intervention, but there are no cohort or experimental studies published. Therefore, we sought to examine self-reported management approaches and practice patterns related to the management of patients with prior surgical lumbar fusion, among United States Veterans Affairs (VA) chiropractors. Methods An electronic survey was administered nationwide to all chiropractors providing clinical care within VA. Questions were informed by a prior survey and piloted on a sample of chiropractors external to VA. Statistical analysis included respondent background information, and quantitative analysis of chiropractic referral patterns and practices. This survey collect information on 1) provider demographics, 2) VA referral patterns, and 3) attitudes, beliefs, practices and interventions utilized by VA chiropractors to manage patients with a history of surgical lumbar fusion. Results The survey response rate was 46.3% (62/134). The respondents were broadly representative of VA chiropractic providers in age, gender, and years in practice. The majority of respondents (90.3%) reported seeing at least 1 post-fusion patient in the past month. The most common therapeutic approaches utilized by VA chiropractors were healthy lifestyle advice (94.9%), pain education (89.8%), exercise prescription (88.1%), stretching (66.1%) and soft tissue manual therapies (62.7%). A relatively smaller proportion described always or frequently incorporating lumbar (16.9%), thoracic (57.6%) or pelvic (39.0%) spinal manipulation. Conclusion This survey provides preliminary data on VA chiropractic services in the management of patients with prior lumbar fusion. These patients are often seen by VA chiropractors, and our findings support the need for further study to advance understanding of interventions utilized by chiropractors in this patient population.

INTRODUCTION: There are no population-level estimates in the United States for achievement of blood pressure goals in patients with diabetes and hypertension by obesity weight class. AIM: We sought to examine the relationship between the extent of obesity and the achievement of guideline-recommended blood pressure goals and other quality of care metrics among patients with diabetes. METHODS: We conducted an observational population-based cohort study of electronic health data of three large health systems from 2010–2012 in rural, urban and suburban settings of 51,229 adults with diabetes. Outcomes were achievement of diabetes quality of care metrics: blood pressure, A1c, and LDL control, and A1c and LDL testing. Two blood pressure goals were examined given the recommendation for adults with diabetes of 130/80 mmHg from JNC7 and the recommendation of 140/90 mmHg from JNC8 in 2014. RESULTS: Patients in obesity classes I, II, and III with diagnosed hypertension were less likely to achieve blood pressure control at both the 140/90 mmHg and 130/80 mmHg control levels. The patients from obesity class III had the lowest likelihood of achieving control at the 130/80 mmHg goal, and control was markedly worse for the 130/80 mmHg threshold in all weight classes. There were minimal to no differences by weight class in LDL and A1c control and LDL and A1c testing. CONCLUSIONS: Although the cardiovascular risk for patients with obesity and diabetes is greater than for non-obese patients with diabetes, we found that patients with obesity are even further behind in achieving blood pressure control.

Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.