The Effects of Video Game Experience and Family Environment on Spatial Memory Development

How children navigate their world, and what environmental cues they use to build a map of their environment has been extensively studied behaviorally. But, with advances in technology, experimental designs have shifted from real environments to virtual environments. This generates an experimental experience that mimics everyday navigation but is easier to control for any confounding variables. To this extent, it has sparked the question of how video game experience influences individual differences in spatial map formation. The present study addresses how video game exposure impacts spatial memory across 6- 12-year-olds and adults. Indices of spatial memory were determined by behavioral measures taken from an object location task (path efficiency, distance error, angular error). Video game experience was collected through a questionnaire that generated scores for active gameplay, virtual layout exposure, and passive watching. An additional factor considered was the effect of family structure on video game experience. This included the amount of family disposable income, parental educational level, and perceived family dynamic. Participants showed a developmental increase in spatial memory accuracy across the age range tested, and participants with greater video game experience demonstrated better spatial memory. However, this was significantly mediated by an interaction with age, whereby only children showed a link between video game experience and spatial memory. While parental education was a significant predictor of video game experience, parental education and perceived family function were not. Additionally, all three measures were non-significant predictors of task performance directly. This study shows that while video game experience is a significant predictor of object location task performance in children, whether social familial factors if any, mediate this relationship are yet to be fully understood.Neuroscienc

Precipitation of carbon from martensite in ductile iron

Since metallographic studies of quenched and tempered cast iron have indicated that the carbon might precipitate directly as graphite rather than as carbides which are found in steels, this investigation used the Debye-Scherrer x-ray diffraction technique to examine electrolytically extracted residues from quenched and tempered ductile cast iron samples to determine if cementite was present. The precipitation of carbon in quenched samples of this ductile cast iron was found to be similar to the precipitation process in steels. Cementite was identified during the early stages of the tempering process when the tempering temperature was 800 degrees F, 900 degrees F, and 1000 degrees F. Cementite was not found in samples tempered at 600 degrees F and 700 degrees F. No attempt was made to identify carbides others than cementite, and it was not determined if transition carbides precipitated at the lower tempering temperatures. The cementite was found to be unstable in the cast iron and decomposed rather rapidly at the tempering temperatures at which it formed. No cementite was present after 30 minutes tempering at 800 degrees F, after 15 minutes at 900 degrees F or after 10 minutes at 1000 degrees F. The hardness was found to continue to drop after all the cementite had graphitized, presumably due to coarsening of the graphite precipitate and possibly also due to additional slow precipitation of carbon which could have graphitized as rapidly as it precipitated --Abstract, pages ii-iii

Where Do People Vape? Insights from Twitter Data.

Background: Emerging evidence suggests that exposure to secondhand and thirdhand aerosol from electronic cigarettes may have serious health risks including respiratory and cardiovascular diseases. Social media data can help identify common locations referenced in vaping-related discussions and offer clues about where individuals vape. These insights can strengthen current tobacco regulations and prioritize new policies to improve public health. This study identified commonly referenced locations in vaping-related discussions on Twitter in 2018. Methods: Vaping-related posts to Twitter were obtained from 1 January 2018 to 31 December 2018. Rule-based classifiers categorized each Twitter post into 11 location-related categories (social venues, living spaces, stores, modes of transportation, schools, workplaces, healthcare offices, eateries, correctional facilities, religious institutions, and miscellaneous) using a data dictionary of location-related keywords (n = 290,816). Results: The most prevalent category was social venues (17.9%), followed by living spaces (16.7%), stores (15.9%), modes of transportation (15.5%), schools (14.9%), and workplaces (11.9%). Other categories pertained to: healthcare offices (2.0%), eateries (1.2%), correctional facilities (0.7%), and religious institutions (0.4%). Conclusion: This study suggests that locations related to socialization venues may be priority areas for future surveillance and enforcement of smoke-free air policies. Similarly, development and enforcement of similar policies at workplaces, schools and multi-unit housing may curb exposure to secondhand and thirdhand aerosol among the public

E-liquid-related posts to Twitter in 2018: Thematic analysis.

IntroductionE-liquid is the solution aerosolized by e-cigarette devices to produce vapor. Continuously evolving e-liquids, and corresponding devices, can affect user experiences associated with these products. Twitter conversations about e-liquids can capture salient behavioral, social, and communicative cues associated with e-liquids. We analyzed Twitter data to characterize key topics of conversation about e-liquids to inform surveillance, and regulatory efforts.MethodsTwitter posts containing e-liquid-related terms ("e-liquid(s)," "e-juice(s)") were obtained from 1 January 2018 to 31 December 2018. Text classifiers were used to identify topics of the posts (n = 15,927).ResultsThe most prevalent topic was Promotional at 29.35% followed by Flavors at 24.22%, and Person Tagging at 21.47%. Juice Composition was next most prevalent at 17.61% followed by Cannabis at 16.83%, and Nicotine Health Risks at 6.39%. Quit Smoking was rare at 0.57%.ConclusionThese results suggest that flavors, cannabis, health risks of nicotine, and composition warrant consideration as targets in future surveillance, public policy, and interventions addressing the use of e-liquids. Twitter provides ample opportunity to influence the normalization, and uptake, of e-cigarette-related products among non-smokers and youth, unless regulatory restrictions, and counter messaging campaigns are developed to reduce this risk

Sternoclavicular joint arthropathy mimicking radiculopathy in a patient with concurrent C4-5 disc herniation

Background Patients with sternoclavicular joint arthropathy, which can result from septic arthritis, often present with localized sternoclavicular pain as well as shoulder pain. Such pain may be similar to the presenting symptoms of cervical intervertebral disc herniation. Clinical presentation A 47-year-old female presented with 1 month of significant pain in the neck as well as right anterior chest and deltoid. The patient was found to have reduced strength in the right deltoid muscle on physical examination. MRI revealed a C4-C5 herniated nucleus pulposus. The patient underwent successful C4-C5 anterior cervical discectomy, but subsequently developed painful swelling in the region of the right sternoclavicular joint with limited motor strength in the right shoulder and arm. A needle biopsy of the mass yielded negative results, but her erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) numbers did respond to antibiotics, consistent with infection of the sternoclavicular joint. A follow-up CT scan (6.5 months postoperatively) revealed apparent resolution right sternoclavicular joint arthropathy, thought the patient continued to experience pain. 15 months postoperatively, the patient was prescribed methotrexate due to persistent pain and mild weakness arising from a possible rheumatologic inflammation. 19 months postoperatively, the patient had full strength of the right shoulder and arm and visible decrease in swelling at the sternoclavicular joint. More than three years postoperatively, the patient was diagnosed with multiple myeloma, which was appropriately treated. At follow-up four years postoperatively, the patient had an MRI showing new C6-C7 herniated nucleus pulposus, but no longer had any right shoulder or chest pain or associated weakness. Conclusion This case demonstrates that sternoclavicular joint arthropathy results in symptoms that can mimic the presenting symptoms of shoulder or cervical spine pathology, such as shoulder and neck pain, necessitating careful diagnosis and management

Social Bots for Online Public Health Interventions

According to the Center for Disease Control and Prevention, in the United States hundreds of thousands initiate smoking each year, and millions live with smoking-related dis- eases. Many tobacco users discuss their habits and preferences on social media. This work conceptualizes a framework for targeted health interventions to inform tobacco users about the consequences of tobacco use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that leverages machine learning to identify users posting pro-tobacco tweets and select individualized interventions to address their interest in tobacco use. We searched the Twitter feed for tobacco-related keywords and phrases, and trained a convolutional neural network using over 4,000 tweets dichotomously manually labeled as either pro- tobacco or not pro-tobacco. This model achieves a 90% recall rate on the training set and 74% on test data. Users posting pro- tobacco tweets are matched with former smokers with similar interests who posted anti-tobacco tweets. Algorithmic matching, based on the power of peer influence, allows for the systematic delivery of personalized interventions based on real anti-tobacco tweets from former smokers. Experimental evaluation suggests that our system would perform well if deployed. This research offers opportunities for public health researchers to increase health awareness at scale. Future work entails deploying the fully operational Notobot system in a controlled experiment within a public health campaign

Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization

We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample importance weighting. Our method is grounded in the principles of distributionally robust optimization (DRO) with Kullback-Leibler divergence. RGD is simple to implement, computationally efficient, and compatible with widely used optimizers such as SGD and Adam. We demonstrate the broad applicability and impact of RGD by achieving state-of-the-art results on diverse benchmarks, including improvements of +0.7% (DomainBed), +1.44% (tabular classification), +1.94% (GLUE with BERT), and +1.01% (ImageNet-1K with ViT)