Effect of Deep Brain Stimulation on Parkinson's Nonmotor Symptoms following Unilateral DBS: A Pilot Study

Parkinson's disease (PD) management has traditionally focused largely on motor symptoms. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) and globus pallidus internus (GPi) are effective treatments for motor symptoms. Nonmotor symptoms (NMSs) may also profoundly affect the quality of life. The purpose of this pilot study was to evaluate NMS changes pre- and post-DBS utilizing two recently developed questionnaires. Methods. NMS-Q (questionnaire) and NMS-S (scale) were administered to PD patients before/after unilateral DBS (STN/GPi targets). Results. Ten PD patients (9 STN implants, 1 GPi implant) were included. The three most frequent NMS symptoms identified utilizing NMS-Q in pre-surgical patients were gastrointestinal (100%), sleep (100%), and urinary (90%). NMS sleep subscore significantly decreased (−1.6 points ± 1.8, P = 0.03). The three most frequent NMS symptoms identified in pre-surgical patients using NMS-S were gastrointestinal (90%), mood (80%), and cardiovascular (80%). The largest mean decrease of NMS scores was seen in miscellaneous symptoms (pain, anosmia, weight change, and sweating) (−7 points ± 8.7), and cardiovascular/falls (−1.9, P = 0.02). Conclusion. Non-motor symptoms improved on two separate questionnaires following unilateral DBS for PD. Future studies are needed to confirm these findings and determine their clinical significance as well as to examine the strengths/weaknesses of each questionnaire/scale

Space and Time in Macroeconomic Panel Data: Young Workers and State-Level Unemployment Revisited

A provocative paper by Shimer (2001) finds that state-level youth shares and unemployment rates are negatively correlated, in contrast to conventional assumptions about demographic effects on labor markets. This paper updates Shimer's regressions and shows that this surprising correlation essentially disappears when the end of the sample period is extended from 1996 to 2005. This shift does not occur because of a change in the underlying economy during the past decade. Rather, the presence of a cross-sectional (that is, spatial) correlation in the state-level data sharply reduces the precision of the earlier estimates, so that the true standard errors are several times larger than those originally reported. Using a longer sample period and some controls for spatial correlation in the regression, point estimates for the youth-share effect on unemployment are positive and close to what a conventional model would imply. Unfortunately, the standard errors remain very large. The difficulty of obtaining precise estimates with these data illustrates a potential pitfall in the use of regional panel data for macroeconomic analysis

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine

Regional Environmental Breadth Predicts Geographic Range and Longevity in Fossil Marine Genera

Geographic range is a good indicator of extinction susceptibility in fossil marine species and higher taxa. The widely-recognized positive correlation between geographic range and taxonomic duration is typically attributed to either accumulating geographic range with age or an extinction buffering effect, whereby cosmopolitan taxa persist longer because they are reintroduced by dispersal from remote source populations after local extinction. The former hypothesis predicts that all taxa within a region should have equal probabilities of extinction regardless of global distributions while the latter predicts that cosmopolitan genera will have greater survivorship within a region than endemics within the same region. Here we test the assumption that all taxa within a region have equal likelihoods of extinction.We use North American and European occurrences of marine genera from the Paleobiology Database and the areal extent of marine sedimentary cover in North America to show that endemic and cosmopolitan fossil marine genera have significantly different range-duration relationships and that broad geographic range and longevity are both predicted by regional environmental breadth. Specifically, genera that occur outside of the focal region are significantly longer lived and have larger geographic ranges and environmental breadths within the focal region than do their endemic counterparts, even after controlling for differences in sampling intensity. Analyses of the number of paleoenvironmental zones occupied by endemic and cosmopolitan genera suggest that the number of paleoenvironmental zones occupied is a key factor of geographic range that promotes genus survivorship.Wide environmental tolerances within a single region predict both broad geographic range and increased longevity in marine genera over evolutionary time. This result provides a specific driving mechanism for the spatial and temporal distributions of marine genera at regional and global scales and is consistent with the niche-breadth hypothesis operating on macroevolutionary timescales

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. Highlights: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research