Local Testing for Membership in Lattices

Motivated by the structural analogies between point lattices and linear error-correcting codes, and by the mature theory on locally testable codes, we initiate a systematic study of local testing for membership in lattices. Testing membership in lattices is also motivated in practice, by applications to integer programming, error detection in lattice-based communication, and cryptography. Apart from establishing the conceptual foundations of lattice testing, our results include the following: 1. We demonstrate upper and lower bounds on the query complexity of local testing for the well-known family of code formula lattices. Furthermore, we instantiate our results with code formula lattices constructed from Reed-Muller codes, and obtain nearly-tight bounds. 2. We show that in order to achieve low query complexity, it is sufficient to design one-sided non-adaptive canonical tests. This result is akin to, and based on an analogous result for error-correcting codes due to Ben-Sasson et al. (SIAM J. Computing 35(1) pp1-21)

Frequency shifts and depth dependence of premotor beta band activity during perceptual decision-making

Neural activity in the premotor and motor cortices shows prominent structure in the beta frequency range (13–30 Hz). Currently, the behavioral relevance of this beta band activity (BBA) is debated. The underlying source of motor BBA and how it changes as a function of cortical depth are also not completely understood. Here, we addressed these unresolved questions by investigating BBA recorded using laminar electrodes in the dorsal premotor cortex of 2 male rhesus macaques performing a visual reaction time (RT) reach discrimination task. We observed robust BBA before and after the onset of the visual stimulus but not during the arm movement. While poststimulus BBA was positively correlated with RT throughout the beta frequency range, prestimulus correlation varied by frequency. Low beta frequencies (∼12–20 Hz) were positively correlated with RT, and high beta frequencies (∼22–30 Hz) were negatively correlated with RT. Analysis and simulations suggested that these frequency-dependent correlations could emerge due to a shift in the component frequencies of the prestimulus BBA as a function of RT, such that faster RTs are accompanied by greater power in high beta frequencies. We also observed a laminar dependence of BBA, with deeper electrodes demonstrating stronger power in low beta frequencies both prestimulus and poststimulus. The heterogeneous nature of BBA and the changing relationship between BBA and RT in different task epochs may be a sign of the differential network dynamics involved in cue expectation, decision-making, motor preparation, and movement execution.Published versio

Patters of use and key predictors for the use of wearable health care devices by US adults: insights from a national survey

Background: Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults. Objective: The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults. Methods: Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis. Results: About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95White individuals (OR 1.65, 95incomes greater than US$75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health. Conclusions: The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society [Abstract copyright: ©Ranganathan Chandrasekaran, Vipanchi Katthula, Evangelos Moustakas. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.10.2020.

Too old for technology? Use of wearable healthcare devices by older adults and their willingness to share health data with providers

Wearable healthcare devices offer tremendous promise to effectively track and improve the well-being of older adults. Yet, little is known about the use of wearable devices by older adults. Drawing upon a national survey in US with 1481 older adults, we examine the use of wearable healthcare devices and the key predictors of use viz. sociodemographic factors, health conditions, and technology self-efficacy. We also examine if the predictors are associated with elders’ willingness to share health data from wearable devices with healthcare providers. We find low level of wearable use (17.49%) among US older adults. We find significant positive associations between technology self-efficacy, health conditions, and demographic factors (gender, race, education, and annual household income) and use of wearable devices. Men were less likely (OR = 0.62, 95% CI 0.36–1.04) and Asians were more likely (OR = 2.60, 95% CI 0.89–7.64) to use wearables, as did healthy adults (OR = 1.98, 95% CI 1.37–2.87). Those who electronically communicated with their doctors (OR = 1.86, 95% CI 1.16–2.97), and those who searched online for health information (OR = 1.79, 95% CI 1.03–3.10) were more likely to use wearables. Though 80.15% of wearable users are willing to share health data with providers, those with greater technology self-efficacy and favorable attitudes toward exercise are more willing

Topics, trends, and sentiments of Tweets about the COVID-19 pandemic: temporal infoveillance study

With restricted movements and stay-at-home orders due to COVID-19 pandemic, social media platforms like Twitter have become an outlet for users to express their concerns, opinions and feelings about the pandemic. Individuals, health agencies and governments are using Twitter to communicate about COVID-19. This research builds on the emergent stream of studies to examine COVID-19 related English tweets covering a time period from Jan 1, 2020 to May 9, 2020. We perform a temporal assessment and examine variations in the topics and sentiment-scores to uncover key trends. To examine key themes and topics from COVID-19 related English tweets posted by individuals, and to explore the trends and variations in how the COVID-19 related tweets, key topics and associated sentiments changed over a period of time before and after the disease was declared as pandemic. Combining data from two publicly available COVID-19 tweet datasets with our own search, we compiled a dataset of 13.9 million COVID-19 related English tweets made by individuals. We use Guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and use VADER sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. Topic modelling yielded 26 topics, grouped into 10 broader themes underlying the COVID-19 tweets. 20.51% of tweets were about COVID-19's impact of economy and markets, followed by spread and growth in cases (15.45%), treatment and recovery (13.14%), impact on healthcare sector (11.40%), and governments' response (11.19%). Average compound sentiment scores were found to be negative throughout the time period of our examination for spread and growth of cases, symptoms, racism, source of the outbreak and political impacts of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on economy and market, governments' response, impact on healthcare industry, treatment and recovery. Identification of dominant themes, topics, sentiments and changing trends about COVID-19 pandemic can help governments, healthcare agencies and policy makers to frame appropriate responses to prevent and control the spread of pandemic

Examining public sentiments and attitudes toward COVID-19 vaccination: infoveillance study using Twitter posts

Background: A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine–related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective: The aim of this study was to examine key themes and topics from COVID-19 vaccine–related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods: We gathered and assessed a corpus of 2.94 million COVID-19 vaccine–related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results: Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy. Conclusions: Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts. [Abstract copyright: ©Ranganathan Chandrasekaran, Rashi Desai, Harsh Shah, Vivek Kumar, Evangelos Moustakas. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 15.04.2022.

Isolation and characterization of glycosaminoglycans from brain of children with protein-calorie malnutrition

The uronic acid containing glycosaminoglycans (GAGs) were isolated from the brains of 1-year-old and 4-year-old kwashiorkor children and characterised by constituent analyses. A marked reduction is the total GAG concentration of brain was noticed in both cases of kwashiorkor. In the 1-year-old kwashiorkor brain, hyaluronic acid is the most predominant GAG (73.5 per cent) whereas heparan sulphate, chondroitin sulphates and low sulphated chondroitin sulphate constituted less than 10 per cent. In the 4-year-old kwashiorkor brain, the proportion of hyaluronic acid was 27.5 per cent, low sulphated chondroitin sulphate 31.2 per cent, chondroitin sulphates 28.3 per cent and heparan sulphate 10 per cent. This marked reduction in the concentration as well as qualitative changes in GAG in protein-calorie malnutrition as compared to the normal is discussed in relation to brain function

Isolation and characterization of glycosaminoglycans in brain of different species

The uronic acid-containing glycosaminoglycans present in the brains of rat, monkey, chicken, sheep and rabbit were isolated into various fractions by combining the cetyl pyridinium procedure and DEAE-Sephadex column chromatography. The analyses of the fractions show that hyaluronic acid, chondroitin-4-sulphate, chondroitin-6-sulphate, heparan sulphate and a testicular hyaluronidase-resistant galactosamine-containing GAG are present in the brain of all the species studied. Hyaluronic acid is the major GAG (33-41 per cent). Chondroitin-4-sulphate (19-35 per cent), and heparan sulphate (11-19 per cent), are the next prominent GAGs, in all the species except chicken. The results indicate the similarity in the pattern of GAGs in the brain of all the species