Scanning electron microscopy and molecular dynamics of surfaces of growing and ablating hexagonal ice crystals

We present the first clearly resolved observations of surfaces of growing and ablating hexagonal ice crystals using variable-pressure scanning electron microscopy. The ice surface develops trans-prismatic strands, separated from one another by distances of 5–10 ?m. The strands are present at a wide range of supersaturations, but are most pronounced at temperatures near the frost point. Pyramidal facets consistent with Miller-Bravais indices of 1011, and possibly also 2021, are associated with ice growth under these conditions. A molecular-dynamics model of a free-standing ice Ih nanocolumn containing 8400 water molecules does not develop trans-prismatic strands, suggesting these features originate at larger spatial or temporal scales. The possible relevance of these surface features to cirrus ice is discussed

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy.Comment: 12 pages, 10 figure

Effect of Opioid vs Nonopioid Medications on Pain-Related Function in Patients With Chronic Back Pain or Hip or Knee Osteoarthritis Pain

Importance: Limited evidence is available regarding long-term outcomes of opioids compared with nonopioid medications for chronic pain. Objective: To compare opioid vs nonopioid medications over 12 months on pain-related function, pain intensity, and adverse effects. Design, Setting, and Participants: Pragmatic, 12-month, randomized trial with masked outcome assessment. Patients were recruited from Veterans Affairs primary care clinics from June 2013 through December 2015; follow-up was completed December 2016. Eligible patients had moderate to severe chronic back pain or hip or knee osteoarthritis pain despite analgesic use. Of 265 patients enrolled, 25 withdrew prior to randomization and 240 were randomized. Interventions: Both interventions (opioid and nonopioid medication therapy) followed a treat-to-target strategy aiming for improved pain and function. Each intervention had its own prescribing strategy that included multiple medication options in 3 steps. In the opioid group, the first step was immediate-release morphine, oxycodone, or hydrocodone/acetaminophen. For the nonopioid group, the first step was acetaminophen (paracetamol) or a nonsteroidal anti-inflammatory drug. Medications were changed, added, or adjusted within the assigned treatment group according to individual patient response. Main Outcomes and Measures: The primary outcome was pain-related function (Brief Pain Inventory [BPI] interference scale) over 12 months and the main secondary outcome was pain intensity (BPI severity scale). For both BPI scales (range, 0-10; higher scores = worse function or pain intensity), a 1-point improvement was clinically important. The primary adverse outcome was medication-related symptoms (patient-reported checklist; range, 0-19). Results: Among 240 randomized patients (mean age, 58.3 years; women, 32 [13.0%]), 234 (97.5%) completed the trial. Groups did not significantly differ on pain-related function over 12 months (overall P = .58); mean 12-month BPI interference was 3.4 for the opioid group and 3.3 for the nonopioid group (difference, 0.1 [95% CI, -0.5 to 0.7]). Pain intensity was significantly better in the nonopioid group over 12 months (overall P = .03); mean 12-month BPI severity was 4.0 for the opioid group and 3.5 for the nonopioid group (difference, 0.5 [95% CI, 0.0 to 1.0]). Adverse medication-related symptoms were significantly more common in the opioid group over 12 months (overall P = .03); mean medication-related symptoms at 12 months were 1.8 in the opioid group and 0.9 in the nonopioid group (difference, 0.9 [95% CI, 0.3 to 1.5]). Conclusions and Relevance: Treatment with opioids was not superior to treatment with nonopioid medications for improving pain-related function over 12 months. Results do not support initiation of opioid therapy for moderate to severe chronic back pain or hip or knee osteoarthritis pain

Design, recruitment outcomes, and sample characteristics of the Strategies for Prescribing Analgesics Comparative Effectiveness (SPACE) trial

This manuscript describes the study protocol, recruitment outcomes, and baseline participant characteristics for the Strategies for Prescribing Analgesics Comparative Effectiveness (SPACE) trial. SPACE is a pragmatic randomized comparative effectiveness trial conducted in multiple VA primary care clinics within one VA health care system. The objective was to compare benefits and harms of opioid therapy versus non-opioid medication therapy over 12 months among patients with moderate-to-severe chronic back pain or hip/knee osteoarthritis pain despite analgesic therapy; patients already receiving regular opioid therapy were excluded. Key design features include comparing two clinically-relevant medication interventions, pragmatic eligibility criteria, and flexible treat-to-target interventions. Screening, recruitment and study enrollment were conducted over 31 months. A total of 4491 patients were contacted for eligibility screening; 53.1% were ineligible, 41.0% refused, and 5.9% enrolled. The most common reasons for ineligibility were not meeting pain location and severity criteria. The most common study-specific reasons for refusal were preference for no opioid use and preference for no pain medications. Of 265 enrolled patients, 25 withdrew before randomization. Of 240 randomized patients, 87.9% were male, 84.1% were white, and age range was 21–80 years. Past-year mental health diagnoses were 28.3% depression, 17% anxiety, 9.4% PTSD, 7.9% alcohol use disorder, and 2.6% drug use disorder. In conclusion, although recruitment for this trial was challenging, characteristics of enrolled participants suggest we were successful in recruiting patients similar to those prescribed opioid therapy in usual care

Centaurs and Scattered Disk Objects in the Thermal Infrared: Analysis of WISE/NEOWISE Observations

The Wide-field Infrared Survey Explorer (WISE) observed 52 Centaurs and scattered disk objects (SDOs) in the thermal infrared, including 15 new discoveries. We present analyses of these observations to estimate sizes and mean optical albedos. We find mean albedos of 0.08 ± 0.04 for the entire data set. Thermal fits yield average beaming parameters of 0.9 ± 0.2 that are similar for both SDO and Centaur sub-classes. Biased cumulative size distributions yield size-frequency distribution power law indices of ~–1.7 ± 0.3. The data also reveal a relation between albedo and color at the 3σ level. No significant relation between diameter and albedos is found

Perceptions, prevalence, and patterns of cannabis use among cancer patients treated at 12 NCI-Designated Cancer Centers

BackgroundThe legal climate for cannabis use has dramatically changed with an increasing number of states passing legislation legalizing access for medical and recreational use. Among cancer patients, cannabis is often used to ameliorate adverse effects of cancer treatment. Data are limited on the extent and type of use among cancer patients during treatment and the perceived benefits and harms. This multicenter survey was conducted to assess the use of cannabis among cancer patients residing in states with varied legal access to cannabis.MethodsA total of 12 NCI-Designated Cancer Centers, across states with varied cannabis-access legal status, conducted surveys with a core questionnaire to assess cannabis use among recently diagnosed cancer patients. Data were collected between September 2021 and August 2023 and pooled across 12 cancer centers. Frequencies and 95% confidence intervals for core survey measures were calculated, and weighted estimates are presented for the 10 sites that drew probability samples.ResultsOverall reported cannabis use since cancer diagnosis among survey respondents was 32.9% (weighted), which varied slightly by state legalization status. The most common perceived benefits of use were for pain, sleep, stress and anxiety, and treatment side effects. Reported perceived risks were less common and included inability to drive, difficulty concentrating, lung damage, addiction, and impact on employment. A majority reported feeling comfortable speaking to health-care providers though, overall, only 21.5% reported having done so. Among those who used cannabis since diagnosis, the most common modes were eating in food, smoking, and pills or tinctures, and the most common reasons were for sleep disturbance, followed by pain and stress and anxiety with 60%-68% reporting improved symptoms with use.ConclusionThis geographically diverse survey demonstrates that patients use cannabis regardless of its legal status. Addressing knowledge gaps concerning benefits and harms of cannabis use during cancer treatment is critical to enhance patient-provider communication

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions