JonDavid Sparks

Impaired Visual Search in Drivers with Parkinson's Disease

To assess the ability for visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with Parkinsons disease (PD).

Driving under low-contrast visibility conditions in Parkinson disease

Objective: To assess driving performance in Parkinson disease (PD) under low-contrast visibility conditions. Methods: Licensed, active drivers with mild to moderate PD (n = 67, aged 66.2 ± 9.0 years, median Hoehn–Yahr stage = 2) and controls (n = 51, aged 64.0 ± 7.2 years) drove in a driving simulator under high- (clear sky) and low-contrast visibility (fog) conditions, leading up to an intersection where an incurring vehicle posed a crash risk in fog. Results: Drivers with PD had higher SD of lateral position (SDLP) and lane violation counts (LVC) than controls during fog (p < 0.001). Transition from high- to low-contrast visibility condition increased SDLP and LVC more in PD than in controls (p < 0.01). A larger proportion of drivers with PD crashed at the intersection in fog (76.1% vs 37.3%, p < 0.0001). The time to first reaction in response to incursion was longer in drivers with PD compared with controls (median 2.5 vs 2.0 seconds, p < 0.0001). Within the PD group, the strongest predictors of poor driving outcomes under low-contrast visibility conditions were worse scores on measures of visual processing speed and attention, motion perception, contrast sensitivity, visuospatial construction, motor speed, and activities of daily living score. Conclusions: During driving simulation under low-contrast visibility conditions, drivers with Parkinson disease (PD) had poorer vehicle control and were at higher risk for crashes, which were primarily predicted by decreased visual perception and cognition; motor dysfunction also contributed. Our results suggest that drivers with PD may be at risk for unsafe driving in low-contrast visibility conditions such as during fog or twilight.

Visual vigilance in drivers with obstructive sleep apnea

OBJECTIVE To determine the effects of obstructive sleep apnea (OSA) on visual vigilance during simulated automobile driving. METHODS Twenty-five drivers with OSA and 41 comparison drivers participated in an hour-long drive in a high-fidelity driving simulator. Drivers responded to light targets flashed at seven locations across the forward horizon. Dependent measures were percent correct [hit rate (HR)] and reaction time (RT). Self-assessment of sleepiness used the Stanford Sleepiness Scale (SSS) before and after the drive and the Epworth Sleepiness Scale (ESS). RESULTS OSA drivers showed reduced vigilance based on lower HR than comparison drivers, especially for peripheral targets (80.7+/-14.8% vs. 86.7+/-8.8%, P=.03). OSA drivers were sleepier at the end of the drive than comparison drivers (SSS=4.2+/-1.2 vs. 3.6+/-1.2, P=.03), and increased sleepiness correlated with decreased HR only in those with OSA (r=-0.49, P=.01). Lower HR and higher post-drive SSS predicted greater numbers of driving errors in all subjects. Yet, ESS, predrive SSS, and most objective measures of disease severity failed to predict driving and vigilance performance in OSA. CONCLUSIONS Reduced vigilance for peripheral visual targets indicates that OSA drivers have restriction of their effective field of view, which may partly explain their increased crash risk. This fatigue-related decline in attention is predicted by increased subjective sleepiness during driving. These findings may suggest a means of identifying and counseling high-risk drivers and aid in the development of in-vehicle alerting and warning devices.

Change blindness, aging, and cognition

Change blindness (CB), the inability to detect changes in visual scenes, may increase with age and early Alzheimers disease (AD). To test this hypothesis, participants were asked to localize changes in natural scenes. Dependent measures were response time (RT), hit rate, false positives (FP), and true sensitivity (d′). Increased age correlated with increased sensitivity and RT; AD predicted even slower RT. Accuracy and RT were negatively correlated. Differences in FP were nonsignificant. CB correlated with impaired attention, working memory, and executive function. Advanced age and AD were associated with increased CB, perhaps due to declining memory and attention. CB could affect real-world tasks, like automobile driving.

First and second-order motion perception after focal human brain lesions.

Perception of visual motion includes a first-order mechanism sensitive to luminance changes and a second-order motion mechanism sensitive to contrast changes. We studied neural substrates for these motion types in 142 subjects with visual cortex lesions, 68 normal controls and 28 brain lesion controls. On first-order motion, the visual cortex lesion group performed significantly worse than normal controls overall and in each hemifield, but second-order motion did not differ. Only one individual showed a selective second-order motion deficit. Motion deficits were seen with lesions outside the small occipito-temporal region thought to contain a human homolog of motion processing area MT (V5), suggesting that many areas of human brain process visual motion.

Missing Data: A Gentle Introduction

intimidating, which stands in contrast to the perceived complexities of entering into the study of bioinformatics. The 10 chapters in Introduction to Bioinformatics are as follows: (1) The Data: Storage and Retrieval, (2) Genome Sequence Analysis, (3) Protein Evolution, (4) Similarity Searches in Databases, (5) Amino Acid Sequence Analysis, (6) Prediction of the Three-Dimensional Structure of a Protein, (7) Homology Modeling, (8) Fold Recognition Methods, (9) New Fold Modeling, and (10) The “Omics” Universe. Chapters are structured such that each is followed by relevant historical and modern references. In addition to the “Historical Contributions” passages, Tramontano has also interspersed short, informative stories and quotations. I must admit that I always enjoy such tidbits as they remind me of what one might encounter during an interesting digression of an actual lecture. In support of each chapter there exist several questions that pertain to applicable problems that the bioinformatician might encounter. Although the preface to Introduction to Bioinformatics describes problem-specific data being available on the publisher’s Web site, I was unable, after a brief search, to locate said resource. I was also unable to find a problem that referred to the absent data; therefore it is most likely not an issue. While the publisher’s intended audience for this book is clear (i.e., nearly everyone!), Tramontano narrows the scope to “students who want to have an idea about what bioinformatics is before deciding whether it is worth getting deeper into the subject,” and those who are considering turning the focus of their career toward bioinformatics-related undertakings. To this extent Introduction to Bioinformatics achieves its goal; however, one may ponder the amount of biological knowledge necessary to fully grasp some of the material. For example, I found the added focus on the three-dimensional protein structure prediction refreshing for an introductory text, but am interested to see how well a prospective bioinformatics student could grasp the material sans any semi-sophisticated biology course in their background. Consider the fact, however, that undergraduate bioinformatics programs are rare and, being a product of such a system, this could be a personal and unique concern. Students with the experience of a collegiate-level biology course should find their background satisfactory for this text. As far as those considering bioinformatics as a part of their future or even current career, Introduction to Bioinformatics would indeed be a fine jumping point due to its well-written, comprehensive coverage, nicely augmented through the “Historical Contributions” and “Suggestions for Further Reading” passages, which often point toward the landmark papers and texts from what some might call the “heavy hitters” of the field. As stated previously, part of the unique lure of Introduction to Bioinformatics is its added focus on all things protein. Indeed, any summary of this work should note the breadth of the material covering proteomics. Tramontano has incorporated a broad coverage of protein science that moves from fundamental basics through methodological discussions of sequence analysis and, especially, folding prediction including short examples, the mentioning of current efforts, and indications of existing limitations. Comparable coverage of proteomics is not easily found in other introductory texts thus revealing the author’s emphasis on this very important area of bioinformatics and setting the text apart from its peers. Tramontano has included in Introduction to Bioinformatics many relevant examples. However, some of the figures from Internet resources such as EST, SwissProt, and GENBANK are generally too vast for such a physically slender book. The font size for these examples is small, thus the reader might be best served by following the citations and observing said submissions online as to not overly strain themselves! Font size aside, the text does not necessarily require the reader to pull information from figures associated with Internet resources, but instead seeks to familiarize the reader with the overall layout of such frequently used information sources. Beyond Internet resource figures there exist many helpful diagrams and examples for understanding both bioinformatics methodologies and the biology from which they are based. Introduction to Bioinformatics serves a noble purpose and is structured and written in a manner that certainly warrants its position in the Chapman & Hall/CRC Mathematical and Computational Biology Series. Said series “seeks to encourage the integration of mathematical, statistical, and computational methods into biology.” Nearly needless to say, this book series is practically screaming for bioinformatics-related submissions. Tramontano’s added emphasis on proteomics should serve as an indication of a major current focus of bioinformatics and also to welcome Introduction to Bioinformatics into the canon of bioinformatics-related literature.