Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016

The UN’s Sustainable Development Goals (SDGs) are grounded in the global ambition of “leaving no one behind”. Understanding today’s gains and gaps for the health-related SDGs is essential for decision makers as they aim to improve the health of populations. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016), we measured 37 of the 50 health-related SDG indicators over the period 1990–2016 for 188 countries, and then on the basis of these past trends, we projected indicators to 2030

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016

Dissecting the precise nature of itch-evoked scratching.

Itch is a discrete and irritating sensation tightly coupled to a drive to scratch. Acute scratching developed evolutionarily as an adaptive defense against skin irritants, pathogens, or parasites. In contrast, the itch-scratch cycle in chronic itch is harmful, inducing escalating itch and skin damage. Clinically and preclinically, scratching incidence is currently evaluated as a unidimensional motor parameter and believed to reflect itch severity. We propose that scratching, when appreciated as a complex, multidimensional motor behavior, will yield greater insight into the nature of itch and the organization of neural circuits driving repetitive motor patterns. We outline the limitations of standard measurements of scratching in rodent models and present new approaches to observe and quantify itch-evoked scratching. We argue that accurate quantitative measurements of scratching are critical for dissecting the molecular, cellular, and circuit mechanisms underlying itch and for preclinical development of therapeutic interventions for acute and chronic itch disorders

Multisensory control of multimodal behavior: do the legs know what the tongue is doing?

Understanding of adaptive behavior requires the precisely controlled presentation of multisensory stimuli combined with simultaneous measurement of multiple behavioral modalities. Hence, we developed a virtual reality apparatus that allows for simultaneous measurement of reward checking, a commonly used measure in associative learning paradigms, and navigational behavior, along with precisely controlled presentation of visual, auditory and reward stimuli. Rats performed a virtual spatial navigation task analogous to the Morris maze where only distal visual or auditory cues provided spatial information. Spatial navigation and reward checking maps showed experience-dependent learning and were in register for distal visual cues. However, they showed a dissociation, whereby distal auditory cues failed to support spatial navigation but did support spatially localized reward checking. These findings indicate that rats can navigate in virtual space with only distal visual cues, without significant vestibular or other sensory inputs. Furthermore, they reveal the simultaneous dissociation between two reward-driven behaviors

Multisensory Control of Multimodal Behavior: Do the Legs Know What the Tongue Is Doing?

<div><p>Understanding of adaptive behavior requires the precisely controlled presentation of multisensory stimuli combined with simultaneous measurement of multiple behavioral modalities. Hence, we developed a virtual reality apparatus that allows for simultaneous measurement of reward checking, a commonly used measure in associative learning paradigms, and navigational behavior, along with precisely controlled presentation of visual, auditory and reward stimuli. Rats performed a virtual spatial navigation task analogous to the Morris maze where only distal visual or auditory cues provided spatial information. Spatial navigation and reward checking maps showed experience-dependent learning and were in register for distal visual cues. However, they showed a dissociation, whereby distal auditory cues failed to support spatial navigation but did support spatially localized reward checking. These findings indicate that rats can navigate in virtual space with only distal visual cues, without significant vestibular or other sensory inputs. Furthermore, they reveal the simultaneous dissociation between two reward-driven behaviors.</p></div

Navigational performance and reward checking during the auditory and visual virtual spatial navigation tasks.

<p>(A) Schematic of the auditory task and the visual task. Symbols as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080465#pone-0080465-g003" target="_blank">Figure 3</a>. (B) Example paths from each task. (C) 2-D histogram of occupancy averaged across rats over 4 sessions for each trial type. (D) 2-D histogram of the normalized check rate averaged across rats over these sessions. White areas indicate insufficient coverage. (E) Percentage of occupancy and check rate in the target quadrant for the two tasks. Two way ANOVA for effect target vs. other quadrants and auditory vs. visual trial types: Effect of quadrant F(1,8)  = 95.16, p< 0.001, Effect of task type: F(1,8)  = 2.08, p<0.001, Interaction of quadrant and task type: F(1,8)  = 2.08, p = 0.002; Effect of quadrant for auditory task: p = 0.097, target quadrant percentage occupancy: 23.9±0.5% vs. mean non-target occupancy: 25.36±0.17%, note that occupancy is slightly decreased in the target quadrant as animals entering the reward zone are teleported out; for visual task: p<0.001, target quadrant percentage occupancy: 59.6±1.15% vs. mean non-target occupancy: 13.5±0.38%, N = 5. (F) Normalized check rate as a function of distance away from the reward zone in radial bins for both trial types. Effect of distance from reward: F(14,112)  = 67.11, p<0.0001, p> 0.05 for effects of task type and interaction.</p

Navigational performance in the virtual audiovisual spatial navigation task.

<p>(A) Schematic of the virtual environment indicating distal auditory and visual cues and a hidden reward zone and the four start locations. (B) Example paths from a single rat from the 1^st session. The color of each path indicates start location, color coded from the arrows in A. (C) Example paths from the same rat from the 6^th session. (D) Acquisition curve of latency to reward across sessions. F(5,35)  = 4.266, p = 0.0061, Session 1 vs. Session 3: p<0.05, N =  6. (E) Acquisition curve of the distance traveled to reward across sessions. F(5,35)  = 3.00, p = 0.0296, Session 1 vs. Session 5: p<0.05. (F) 2-D histogram of mean occupancy averaged across final four task sessions of asymptotic performance with the smaller reward zone. (G) Example of a probe trial path. (H) Percentage quadrant measures for occupancy time during the final four task sessions performance and during the probe trial. Effect of quadrant: F(3,23)  = 10.15, p = 0.007, F(3,23)  = 10.9, p = 0.0005, respectively.</p

Multisensory contribution to virtual spatial navigation and reward checking.

<p>(A) Schematic of the Audiovisual (AV), Visual (V) only and Auditory (A) virtual spatial mazes. Symbols as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080465#pone-0080465-g001" target="_blank">Figure 1</a>. (B) Example paths for the three trial types from a single rat. The color of each path indicates start location, color coded from the arrows in A. (C) Median latency and distance to reward for each trial type. Effect of trial type, F(2,17)  = 7.555, p = 0.01, F(2,17)  = 8.911, p = 0.006, respectively. A vs. AV and V: p<0.05 for both measures, N = 6. (D) Percentage occupancy in the target quadrant for the three trial types. Effect of trial type: F(2,17)  = 13.19, p = 0.0064. A vs. AV: p = 0.013, A and V: p = 0.018.</p