Physical activity and pain in people with and without cancer.

BACKGROUND: Performing physical activity may provide analgesic benefit, although this effect is more established for noncancer pain rather than cancer pain. The relationship between physical activity and pain outcomes in adults with and without a history of cancer was examined. METHODS: Totals of 51,439 adults without a cancer history and 10,651 adults with a cancer history from the Cancer Prevention Study II Nutrition Cohort were included. Exposures included self-reported moderate to vigorous physical activity (MVPA) as well as 2-year change in MVPA. Pain outcomes included pain intensity (primary outcome) and analgesic use (secondary outcome). RESULTS: MVPA was inversely associated with pain intensity for adults with (odds ratio [OR], 0.84 [≥15 metabolic equivalent of task (MET) h/week vs. <7.5 MET h/week]; 95% confidence interval [CI], 0.76-0.93) and without (OR, 0.79; 95% CI, 0.75-0.82) a history of cancer. Compared to remaining inactive, participants who became sufficiently active (cancer: OR, 0.76; 95% CI, 0.68-0.86; no cancer: OR, 0.73; 95% CI, 0.69-0.77), became inactive (cancer: OR, 0.79; 95% CI, 0.71-0.88; no cancer: OR, 0.84; 95% CI, 0.80-0.89), or remained sufficiently active (cancer: OR, 0.66; 95% CI, 0.60-0.72; no cancer: OR, 0.62; 95% CI, 0.60-0.65) also reported less pain. Physical activity was not related to analgesic use. CONCLUSIONS: The relationship between physical activity and pain intensity was not substantially different between people with and without a history of cancer. Cancer survivors who perform more activity, or who increase their activity, may experience less pain than cancer survivors who consistently perform less. PLAIN LANGUAGE SUMMARY: People who have had cancer often experience ongoing pain. Being physically active may help reduce the intensity of the pain they experience.

Long-term multimorbidity trajectories in older adults: The role of cancer, demographics, and health behaviors.

BACKGROUND: Multimorbidity is associated with premature mortality and excess health care costs. The burden of multimorbidity is highest among patients with cancer, yet trends and determinants of multimorbidity over time are poorly understood. METHODS: Via Medicare claims linked to Cancer Prevention Study II data, group-based trajectory modeling was used to compare National Cancer Institute comorbidity index score trends for cancer survivors and older adults without a cancer history. Among cancer survivors, multinomial logistic regression analyses evaluated associations between demographics, health behaviors, and comorbidity trajectories. RESULTS: In 82,754 participants (mean age, 71.6 years [SD, 5.1 years]; 56.9% female), cancer survivors (n = 11,265) were more likely than older adults without a cancer history to experience the riskiest comorbidity trajectories: (1) steady, high comorbidity scores (remain high; odds ratio [OR], 1.36; 95% CI, 1.29-1.45), and (2) high scores that increased over time (start high and increase; OR, 1.51; 95% CI, 1.38-1.65). Cancer survivors who were physically active postdiagnosis were less likely to fall into these two trajectories (OR, 0.73; 95% CI, 0.64-0.84, remain high; OR, 0.42; 95% CI, 0.33-0.53, start high and increase) compared to inactive survivors. Cancer survivors with obesity were more likely to have a trajectory that started high and increased (OR, 2.83; 95% CI, 2.32-3.45 vs. normal weight), although being physically active offset some obesity-related risk. Cancer survivors who smoked postdiagnosis were also six times more likely to have trajectories that started high and increased (OR, 6.86; 95% CI, 4.41-10.66 vs. never smokers). CONCLUSIONS: Older cancer survivors are more likely to have multiple comorbidities accumulated at a faster pace than older adults without a history of cancer. Weight management, physical activity, and smoking avoidance postdiagnosis may attenuate that trend.

Self-Reported Eating-Occasion Frequency and Timing are Reproducible and Relatively Valid in the American Cancer Society Cancer Prevention Study-3 Diet Assessment Substudy.

BACKGROUND: Accurate assessment of eating-occasion behaviors, such as timing, frequency, and consumption intervals, is important for evaluating associations with obesity and other chronic diseases. OBJECTIVES: The main objective of this study was to assess the relative validity of a 24-hour grid approach to assess eating-occasion timing and frequency in comparison to data derived from repeated 24-hour dietary recalls (DRs). A second objective was to assess the 1-year test-retest reproducibility of the 24-hour grid. METHODS: Between 2015 and 2016, 626 participants in the Cancer Prevention Study-3 (CPS-3) Diet Assessment Substudy (mean age, 52 years; age range, 31-70 years; 64% female; 64% non-Hispanic white, 22% non-Hispanic black, 14% Hispanic) completed 2 grids and up to 6 unannounced, telephone, interviewer-administered DRs over 1 year. Spearman correlations (ρ; 95% CIs) were calculated to assess reproducibility between the repeated eating-occasion grid-derived variables (e.g., numbers of snacks and meals per day, timing of eating occasions) and to assess relative validity by comparing the meal grid and DR-derived summary data separately for weekdays and weekend days. RESULTS: Reproducibility correlations for eating-occasion variables derived from the eating-occasion grids completed 1 year apart were ≥0.5 for the majority of variables analyzed for both weekdays and weekend days, including numbers of snacks and meals per day and timing of the first and last eating occasions of the day. Relative validity was highest among weekday variables and was ≥0.5 for the majority of variables, with correlations ranging from ρ values of 0.32 (number of meals per day) to 0.68 (hour of the first eating occasion). CONCLUSIONS: These findings suggest the eating-occasion grid used in CPS-3 has good reproducibility over 1 year and yields estimates comparable to those from a more detailed method of assessment of eating timing and frequency.

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility.

Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for redocking to an existing cocrystallized protein structure, ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Flexible protein-ligand docking still remains a significant challenge to computational drug design. To target this challenge, we present a deep learning (DL) model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of iterative search algorithms obsolete. The model was trained on a large-scale data set of protein-ligand complexes and evaluated on independent test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.

Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.

Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent developments in generative deep neural networks rendered possible the parallel, statistically independent sampling of molecular configurations. To be able to accurately generate samples of large molecular systems from a high-dimensional multimodal equilibrium distribution function, we developed a hierarchical approach based on expressive normalizing flows with rational quadratic neural splines and coarse-grained representation. Furthermore, system specific priors and adaptive and property-based controlled learning was designed to diminish the likelihood for the generation of high-energy structures during sampling. Finally, backmapping from a coarse-grained to fully atomistic representation is performed through an equivariant transformer model. We demonstrate the applicability of the method on the one-shot configurational sampling of a protein system with more than a hundred amino acids. The results show enhanced expressivity that diminish the invertibility constraints inherent in the normalizing flow framework. Moreover, the capacity of the hierarchical normalizing flow model was tested on a challenging case study of the folding/unfolding dynamics of the peptide chignolin.

Facilitators and Barriers to Conducting Environmental Assessments for Food Establishment Outbreaks, National Environmental Assessment Reporting System, 2014-2016.

Environmental health specialists often perform environmental assessments (EAs) when a suspected or confirmed foodborne illness outbreak is linked to a food establishment. Information from EAs helps officials determine the cause of the outbreak and develop strategies to prevent future outbreaks; however, EAs are not always conducted. To determine facilitators and barriers to conducting EAs, we analyzed open-ended responses reported to the National Environmental Assessment Reporting System about these assessments. We found that EAs were conducted most often when illness was identified, a jurisdiction had a policy to investigate illnesses, and there were resources for such a response. EAs were not conducted in instances such as limited resources, insufficient training, uncooperative facility personnel, or if the establishment fell outside of health department jurisdiction. Identifying the facilitators and barriers to conducting EAs can enable health departments to develop strategies that improve their ability to conduct EAs.

Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites.

Molecular dynamics (MD) simulations allow for accurate prediction of the thermodynamic profile of binding-site water molecules critical for protein-ligand association. Whereas this hydration-site profiling converges rapidly for solvent-exposed sites independent of the initial water placement, an accurate and reliable placement is required for water molecules in occluded binding sites. Here, we present an accurate and efficient hydration-site prediction method for occluded binding sites combining water placement based on 3D-RISM and MD simulations using WATsite.

Restaurant and Staff Characteristics Related to Practices that Could Contribute to Cross-Contamination.

Foodborne illness is a persistent public health concern in the U.S.; over 800 foodborne illness outbreaks are reported to the Centers for Disease Control and Prevention (CDC) annually. Most of these outbreaks (60%) are linked with restaurants. Contamination of food with foodborne pathogens during preparation and storage is a significant contributing factor to many of these outbreaks. The CDC's Environmental Health Specialists Network (EHS-Net) collected data to identify restaurant characteristics, policies, and practices associated with contamination prevention practices. Data collectors interviewed managers and conducted kitchen observations in 312 restaurants across six EHS-Net sites in five states. Data collectors observed at least one food worker action that could lead to contamination in 63.1% of restaurants. The most frequently observed action that could lead to contamination was bare-hand or dirty glove contact with ready-to-eat food (35.9%). The estimated mean number of observed potential contamination actions was greater in restaurants that were independently owned (does not share a name and operations with other restaurants), did not require managers to be certified in food safety, did not have workers trained in food safety, did not have a handwashing policy, did not have a policy minimizing bare-hand contact with ready-to-eat foods, and had a manager with more than two years of experience at their current restaurant. These results suggest that to improve contamination prevention, the foodservice industry and food safety officials can consider supporting and encouraging strong food safety training and policies, particularly concerning hand hygiene, and targeting interventions to independent restaurants.

Benign Breast Disease, NSAIDs, and Postmenopausal Breast Cancer Risk in the CPS-II Cohort.

ABSTRACT: Nonsteroidal anti-inflammatory agents (NSAID) are associated with modest inconsistent reductions in breast cancer risk in population-based cohorts, whereas two focused studies of patients with benign breast disease (BBD) have found lower risk with NSAID use. Given that BBD includes fibroinflammatory lesions linked to elevated breast cancer risk, we assessed whether NSAID use was associated with lower breast cancer risk among patients with BBD.Participants were postmenopausal women in the Cancer Prevention Study-II (CPS-II), a prospective study of cancer incidence and mortality, who completed follow-up surveys in 1997 with follow-up through June 30, 2015. History of BBD, NSAID use, and covariate data were updated biennially. This analysis included 23,615 patients with BBD and 36,751 patients with non-BBD, including 3,896 incident breast cancers over an average of 12.72 years of follow-up among participants. NSAID use, overall and by formulation, recency, duration, and pills per month was analyzed versus breast cancer risk overall and by BBD status using multivariable-adjusted Cox models; BBD status and NSAID use were modeled as time-dependent exposures.Patients with BBD who reported using NSAIDs experienced lower breast cancer risk (HR, 0.87; 95% CI, 0.78-0.97), with similar effects for estrogen receptor (ER)-positive breast cancers [HR, 0.85; 95% confidence interval (CI), 0.74-0.97] and ER-negative breast cancers (HR, 0.87; 95% CI, 0.59-1.29); among women without BBD, NSAID use was unrelated to risk (HR, 1.02; 95% CI, 0.92-1.13; Pinteraction = 0.04). Associations stratified by age, obesity, menopausal hormone use, and cardiovascular disease were similar.Among patients with BBD, NSAID use appears linked to lower breast cancer risk. Further studies to assess the value of NSAID use among patients with BBD are warranted. PREVENTION RELEVANCE: We examined whether NSAID use, a modifiable exposure, is associated with breast cancer risk in postmenopausal women from the Cancer Prevention Study-II with self-reported benign breast disease, an often inflammatory condition associated with higher rates of breast cancer.

Fracture Risk Among Older Cancer Survivors Compared With Older Adults Without a History of Cancer.

Importance: The number of cancer survivors living in the US is projected to be 26.1 million by 2040. Cancer survivors may be at increased risk of bone fractures, but research is limited in several important ways. Objective: To investigate the associations of cancer diagnoses, including time since diagnosis and stage at diagnosis, with risks of pelvic, radial, and vertebral fractures (separately and combined) among older cancer survivors and compared with fracture risk among older adults without a history of cancer. Secondarily, to examine differences in risk of fracture stratified by modifiable behaviors, treatment, and cancer type. Design, Setting, and Participants: This longitudinal cohort study used data from 92 431 older adults in the US Cancer Prevention Study II Nutrition Cohort linked with 1999 to 2017 Medicare claims. Data were analyzed from July 15, 2021, to May 3, 2022. Exposures: Cancer history, time since cancer diagnosis, and stage at cancer diagnosis. Main Outcomes and Measures: Hazard ratios (HRs) and 95% CIs for the risk of pelvic, radial, vertebral, and total frailty-related fractures were estimated using multivariate Cox proportional hazards regression. Stratification was used for secondary aims. Results: Among 92 431 participants (mean [SD] age, was 69.4 [6.0] years, 51 820 [56%] women, and 90 458 [97.9%] White], 12 943 participants experienced a frailty-related bone fracture. Compared with participants without a history of cancer, cancer survivors who were diagnosed 1 to less than 5 years earlier with advanced stage cancer had higher risk of fracture (HR, 2.12; 95% CI, 1.75-2.58). The higher fracture risk in cancer survivors with recent advanced stage diagnosis (vs no cancer) was driven largely by vertebral (HR, 2.46; 95% CI, 1.93-3.13) and pelvic (HR, 2.46; 95% CI, 1.84-3.29) fracture sites. Compared with cancer survivors who did not receive chemotherapy, survivors who received chemotherapy were more likely to have a fracture; this association was stronger within 5 years of diagnosis (HR, 1.31; 95% CI, 1.09-1.57) than 5 or more years after diagnosis (HR, 1.22; 95% CI, 0.99-1.51). Although the HR for risk of fracture was lower among physically active cancer survivors 5 or more years after diagnosis (HR, 0.76; 95% CI, 0.54-1.07), this result was not statistically significant, whereas current smoking was significantly associated with higher risk of fracture (HR, 2.27; 95% CI, 1.55-3.33). Conclusions and Relevance: Findings from this cohort study suggest that older adults with a history of cancer may benefit from clinical guidance on prevention of frailty-related fractures. If study findings are replicated, fracture prevention programs for survivors might include referrals for physical activity with cancer exercise professionals and smoking cessation programs.

COVID-19 and Tweets About Quitting Cigarette Smoking: Topic Model Analysis of Twitter Posts 2018-2020.

Background: The risk of infection and severity of illness by SARS-CoV-2 infection is elevated for people who smoke cigarettes and may motivate quitting. Organic public conversations on Twitter about quitting smoking could provide insight into quitting motivations or behaviors associated with the pandemic. Objective: This study explored key topics of conversation about quitting cigarette smoking and examined their trajectory during 2018-2020. Methods: Topic model analysis with latent Dirichlet allocation (LDA) identified themes in US tweets with the term "quit smoking." The model was trained on posts from 2018 and was then applied to tweets posted in 2019 and 2020. Analysis of variance and follow-up pairwise tests were used to compare the daily frequency of tweets within and across years by quarter. Results: The mean numbers of daily tweets on quitting smoking in 2018, 2019, and 2020 were 133 (SD 36.2), 145 (SD 69.4), and 127 (SD 32.6), respectively. Six topics were extracted: (1) need to quit, (2) personal experiences, (3) electronic cigarettes (e-cigarettes), (4) advice/success, (5) quitting as a component of general health behavior change, and (6) clinics/services. Overall, the pandemic was not associated with changes in posts about quitting; instead, New Year's resolutions and the 2019 e-cigarette or vaping use-associated lung injury (EVALI) epidemic were more plausible explanations for observed changes within and across years. Fewer second-quarter posts in 2020 for the topic e-cigarettes may reflect lower pandemic-related quitting interest, whereas fourth-quarter increases in 2020 for other topics pointed to a late-year upswing. Conclusions: Twitter posts suggest that the pandemic did not generate greater interest in quitting smoking, but possibly a decrease in motivation when the rate of infections was increasing in the second quarter of 2020. Public health authorities may wish to craft messages for specific Twitter audiences (eg, using hashtags) to motivate quitting during pandemics.

Racial, Ethnic, and Nativity Disparities in Physical Activity and Sedentary Time among Cancer Prevention Study-3 Participants.

PURPOSE: Understanding racial/ethnic and nativity disparities in physical activity (PA) is important, as certain subgroups bear a disproportionate burden of physical inactivity-related diseases. This descriptive study compared mean leisure-time moderate-to-vigorous intensity physical activity (LTMVPA) by race/ethnicity and nativity. METHODS: The Cancer Prevention Study-3 (78.1% women; age, 47.9 ± 9.7 yr) includes 4722 (1.9%) Asian/Pacific Islander; 1232 (0.5%) Black/Indigenous (non-White) Latino; 16,041 (6.5%) White Latino; 9295 (3.8%) non-Latino Black; 2623 (1.1%) Indigenous American; and 210,504 (85.7%) non-Latino White participants across the United States and Puerto Rico. Participants completed validated LTMVPA and 24-h time use surveys at enrollment (2006-2013). Differences in LTMVPA across race/ethnicity and nativity were examined by ANCOVA with paired Tukey tests adjusting for age and sex. The proportion of time spent sitting, sleeping, and on PA by race/ethnicity was also compared. RESULTS: There were significant differences in LTMVPA by race/ethnicity (race main effect, P < 0.001; nativity, P = 0.072; interaction, P < 0.001). Pairwise comparisons showed that White participants born abroad were the most active (23.8 MET-h·wk-1) and non-White Latino participants born abroad were the least active (17.9 MET-h·wk-1). Among Latinos, participants born in Puerto Rico were 6.6-7.3 MET-h·wk-1 less active than participants born in Mexico, the United States/Canada, or other countries. There were variations in time use by race/ethnicity, with the largest difference in time spent sitting while watching TV. Black participants spent 14.8% of the day (~3.5 h) sitting watching TV, which was 78 min longer than Asian/Pacific Islander participants. CONCLUSIONS: This study suggests that there are differences in LTMVPA accumulation by race, ethnicity, and nativity. Results can be used to identify demographic groups that may benefit from culturally tailored PA interventions.

Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning.

Accurate and efficient prediction of protein-ligand interactions has been a long-lasting dream of practitioners in drug discovery. The insufficient treatment of hydration is widely recognized to be a major limitation for accurate protein-ligand scoring. Using an integration of molecular dynamics simulations on thousands of protein structures with novel big-data analytics based on convolutional neural networks and deep Taylor decomposition, we consistently identify here three different patterns of hydration to be essential for protein-ligand interactions. In addition to desolvation and water-mediated interactions, the formation of enthalpically favorable networks of first-shell water molecules around solvent-exposed ligand moieties is identified to be essential for protein-ligand binding. Despite being currently neglected in drug discovery, this hydration phenomenon could lead to new avenues in optimizing the free energy of ligand binding. Application of deep neural networks incorporating hydration to docking provides 89% accuracy in binding pose ranking, an essential step for rational structure-based drug design.

Foodborne Illness Outbreaks at Retail Establishments - National Environmental Assessment Reporting System, 16 State and Local Health Departments, 2014-2016.

PROBLEM/CONDITION: State and local public health departments report hundreds of foodborne illness outbreaks each year to CDC and are primarily responsible for investigations of these outbreaks. Typically, investigations involve epidemiology, laboratory, and environmental health components. Health departments voluntarily report epidemiologic and laboratory data from their foodborne illness outbreak investigations to CDC through the Foodborne Disease Outbreak Surveillance System (FDOSS); however, minimal environmental health data from outbreak investigations are reported to FDOSS. PERIOD COVERED: 2014-2016. DESCRIPTION OF SYSTEM: In 2014, CDC launched the National Environmental Assessment Reporting System (NEARS) to complement FDOSS surveillance and to use these data to enhance prevention efforts. State and local health departments voluntarily report data from their foodborne illness outbreak investigations of retail food establishments. These data include characteristics of foodborne illness outbreaks (e.g., agent), characteristics of establishments with outbreaks (e.g., number of meals served daily), food safety policies and practices of these establishments (e.g., glove use policies), and characteristics of outbreak investigations (e.g., timeliness of investigation activities). NEARS is the only available data source that includes characteristics of retail establishments with foodborne illness outbreaks. RESULTS: During 2014-2016, a total of 16 state and local public health departments reported data to NEARS on 404 foodborne illness outbreaks at retail establishments. The majority of outbreaks with a suspected or confirmed agent were caused by norovirus (61.1%). The majority of outbreaks with identified contributing factors had at least one factor associated with food contamination by a worker who was ill or infectious (58.6%). Almost half (47.4%) of establishments with outbreaks had a written policy excluding ill workers from handling food or working. Approximately one third (27.7%) had a written disposable glove use policy. Paid sick leave was available for at least one worker in 38.3% of establishments. For most establishments with outbreaks (68.7%), environmental health investigators initiated their component of the investigation soon after learning about the outbreak (i.e., the same day) and completed their component in one or two visits to the establishment (75.0%). However, in certain instances, contacting the establishment and completing the environmental health component of the investigation occurred much later (>8 days). INTERPRETATION: Most outbreaks reported to NEARS were caused by norovirus, and contamination of food by workers who were ill or infectious contributed to more than half of outbreaks with contributing factors; these findings are consistent with findings from other national outbreak data sets and highlight the role of workers in foodborne illness outbreaks. The relative lack of written policies for ill workers and glove use and paid sick leave for workers in establishments with outbreaks indicates gaps in food safety practices that might have a role in outbreak prevention. The environmental health component of the investigation for most outbreaks was initiated quickly, yet the longer initiation timeframe for certain outbreaks suggests the need for improvement. PUBLIC HEALTH ACTION: Retail establishments can reduce viral foodborne illness outbreaks by protecting food from contamination through proper hand hygiene and excluding workers who are ill or infectious from working. NEARS data can help prioritize training and interventions for state and local food safety programs and the retail food establishment industry by identifying gaps in food safety policies and practices and types of establishments vulnerable to outbreaks. Improvement of certain outbreak investigation practices (e.g., delayed initiation of environmental health investigations) can accelerate identification of the agent and implementation of interventions. Future analysis comparing establishments with and without outbreaks will contribute knowledge about how establishments' characteristics and food safety policies and practices relate to foodborne illness outbreaks and provide information to develop effective prevention approaches.

Psychosocial and cessation-related differences between tobacco-marijuana co-users and single product users in a college student population.

Limited research has examined psychosocial factors that differ among cigarette users, marijuana users, and co-users and influence their cessation efforts. We examined: 1) sociodemographic, mental health, and other substance use in relation to user category; and 2) associations among these factors in relation to recent quit attempts and readiness to quit among single product versus co-users. We used a cross-sectional design to study college students aged 18-25 from seven Georgia campuses, focusing on the 721 reporting cigarette and/or marijuana use in the past 4months (238 cigarette-only; 331 marijuana-only; 152 co-users). Multinomial logistic regression showed that correlates (p's<0.05) of cigarette-only versus co-use included attending public or technical colleges (vs. private) and not using little cigars/cigarillos (LCCs), e-cigarettes, and alcohol. Correlates of marijuana-only versus co-use included being Black or Hispanic (vs. White), not attending technical school, and not using LCCs and e-cigarettes. Importance was rated higher for quitting cigarettes versus marijuana, but confidence was rated lower for quitting cigarettes versus marijuana (p's<0.001). Co-users were more likely to report readiness to quit and quit attempts of cigarettes versus marijuana (p's<0.001). While 23.26% of marijuana-only and 15.13% of cigarette-only users reported readiness to quit, 41.18% of cigarette-only and 21.75% of marijuana-only users reported recent quit attempts (p's<0.001). Binary logistic regressions indicated distinct correlates of readiness to quit and quit attempts of cigarettes and marijuana. Cessation efforts of the respective products must attend to co-use with the other product to better understand relative perceptions of importance and confidence in quitting and actual cessation efforts.

Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.

Myoelectric signals can be used to predict the intended movements of an amputee for prosthesis control. However, untrained effects like limb position changes influence myoelectric signal characteristics, hindering the ability of pattern recognition algorithms to discriminate among motion classes. Despite frequent and long training sessions, these deleterious conditional influences may result in poor performance and device abandonment. GOAL: We present a robust sparsity-based adaptive classification method that is significantly less sensitive to signal deviations resulting from untrained conditions. METHODS: We compare this approach in the offline and online contexts of untrained upper-limb positions for amputee and able-bodied subjects to demonstrate its robustness compared against other myoelectric classification methods. RESULTS: We report significant performance improvements () in untrained limb positions across all subject groups. SIGNIFICANCE: The robustness of our suggested approach helps to ensure better untrained condition performance from fewer training conditions. CONCLUSIONS: This method of prosthesis control has the potential to deliver real-world clinical benefits to amputees: better condition-tolerant performance, reduced training burden in terms of frequency and duration, and increased adoption of myoelectric prostheses.

Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.

Reasons for Polytobacco Use among Young Adults: Scale Development and Validation.

INTRODUCTION: Limited research has examined reasons for polytobacco use, an increasing public health problem, particularly among young adults. We examined reasons for polytobacco use among users of more than one tobacco product in the past 4 months enrolled in an ongoing six-wave longitudinal study of 3,418 students aged 18-25 from seven US colleges and universities. METHODS: An expert panel generated items related to reasons for polytobacco use, included in Wave 3 (administered in Summer 2015). Participants reporting use of more than one tobacco product in the past four months (n=540) were asked to complete the Reasons for Polytobacco Use scale and measures related to tobacco/nicotine use/dependence, use motives, perceptions of tobacco, parental/friend use, other substance use, and mental health. We conducted a factor analysis and then examined convergent and discriminant validity for the derived factors. RESULTS: Our sample was an average age of 20.40 (SD=1.84), 48.0% male, and 21.9% Black. Four factors were identified: Instrumentality, Social Context, Displacement, and Experimentation. Instrumentality was the only factor associated with little cigar/cigarillo and marijuana use. Displacement and Social Context showed similar associations; however, Social Context was associated with having friends who used tobacco while Displacement was not. Experimentation was associated with greater perceived addictiveness and harm of using tobacco products as well as greater perceived social acceptability of tobacco use. CONCLUSIONS: Each of the four factors identified demonstrated unique convergent and discriminant validity. The use of this scale to characterize polytobacco using young adults may help inform and target cessation or prevention interventions.

Real-Time Arm Tracking for HMI Applications.

Limb tracking is an important aspect of human-machine interfaces (HMI). These systems, however, can often be limited by complex algorithms requiring significant processing power, obtrusive and immobile sensing techniques, and high costs. In this work, we utilize a sensor fusion algorithm implemented in commercial inertial measurement units (IMU) to combine accelerometer and gyroscope measurements in an effort to minimize computational requirements of the limb tracking system. In addition, previously developed methods were implemented to eliminate sensor drift by including information from a magnetometer. We tested the accuracy of our system by computing the root mean squared error (RMSE) of the true angle between the headings of two sensors and the estimate of that angle through quaternion-vector manipulations. An average RMSE of approximately 2.9° was achieved. Our limb tracking system is wearable, minimally complex, low-cost, and simple to use which has proven useful in multiple HMI applications discussed herein.