Common Baseline Research Articles

Test-Retest Reliability of Concussion Baseline Assessments in United States Service Academy Cadets: A Report from the National Collegiate Athletic Association (NCAA)-Department of Defense (DoD) CARE Consortium.

In response to advancing clinical practice guidelines regarding concussion management, service members, like athletes, complete a baseline assessment prior to participating in high-risk activities. While several studies have established test stability in athletes, no investigation to date has examined the stability of baseline assessment scores in military cadets. The objective of this study was to assess the test-retest reliability of a baseline concussion test battery in cadets at U.S. Service Academies. All cadets participating in the Concussion Assessment, Research, and Education (CARE) Consortium investigation completed a standard baseline battery that included memory, balance, symptom, and neurocognitive assessments. Annual baseline testing was completed during the first 3 years of the study. A two-way mixed-model analysis of variance (intraclass correlation coefficent (ICC)3,1) and Kappa statistics were used to assess the stability of the metrics at 1-year and 2-year time intervals. ICC values for the 1-year test interval ranged from 0.28 to 0.67 and from 0.15 to 0.57 for the 2-year interval. Kappa values ranged from 0.16 to 0.21 for the 1-year interval and from 0.29 to 0.31 for the 2-year test interval. Across all measures, the observed effects were small, ranging from 0.01 to 0.44. This investigation noted less than optimal reliability for the most common concussion baseline assessments. While none of the assessments met or exceeded the accepted clinical threshold, the effect sizes were relatively small suggesting an overlap in performance from year-to-year. As such, baseline assessments beyond the initial evaluation in cadets are not essential but could aid concussion diagnosis.

RELAY study of erlotinib (ERL) + ramucirumab (RAM) or placebo (PL) in EGFR-mutated metastatic non-small cell lung cancer (NSCLC): Biomarker analysis using circulating tumor DNA (ctDNA) in Japanese patients (pts).

9527 Background: The phase III RELAY study (NCT02411448) showed significantly improved progression-free survival (PFS) for RAM+ERL vs PL+ERL in 449 pts with previously untreated EGFR mutation-positive metastatic NSCLC (median PFS 19.4 vs 12.4 mo, HR 0.591 [95% CI 0.461–0.760], p<.0001). To understand baseline genetic mutations and treatment-emergent (TE) resistance mechanisms, this exploratory liquid biopsy substudy examined biomarkers in ctDNA from participating Japanese pts by next-generation sequencing (NGS) and droplet digital PCR (ddPCR). Methods: Plasma samples were collected at baseline, during treatment (Cycle 4, 13, and every 6 cycles to Cycle 53) and post-study treatment discontinuation (30-day follow-up [30d FU]). Mutations were assessed at baseline and 30d FU by NGS (Ion AmpliSeq Colon and Lung Cancer panel). EGFR mutations and MET and ERBB2 copy number (CN) were assessed at all time points by ddPCR. Baseline markers were analyzed in pts with any detectable baseline mutation (to confirm ctDNA presence; NGS N=84, ddPCR N=74). TE mutations were analyzed in pts with any detectable mutation at baseline and 30d FU (NGS N=26, ddPCR N=28). Among these pts, 81% and 57% for NGS and ddPCR, respectively, had progressed by 30d FU. Results: By plasma NGS, baseline EGFR activating mutations (exon 19 deletion or exon 21 [L858R] mutation) were detected in 83.3% of pts. Common co-occurring baseline mutations were TP53 (42.9%), PTEN (7.1%) and KRAS (6.0%). Baseline TP53 mutation rate was higher in men vs women (p=.02). No difference in PFS was detected by baseline TP53 status (interaction predictive p=.45, prognostic p=.33). TE mutations were detected in EGFR (including T790M), FGFR3, KRAS and TP53. Slightly higher rates of TE KRAS (p=.03) and TP53 (p=.07) mutations were detected in RAM+ERL than in PL+ERL. TE total EGFR mutations (p=.65) or TE T790M (p=.69) did not differ by arm. By ddPCR, baseline EGFR activating mutations were detected in all pts. T790M was detected at baseline in 2/37 pts/arm (5.4%) and was TE in 6/11 (55%) RAM+ERL pts and 7/17 (41%) PL+ERL pts. There was a trend (p=.054) for greater ERBB2 CN in RAM+ERL vs PL+ERL at Cycle 4. MET CN decreased slightly at Cycle 4 in both arms (significant in PL+ERL, p=.003). Biomarker levels by ddPCR across all time points will be presented. Conclusions: Though limited by sample size and likely inconsistent tumor shedding, this exploratory study identified potential differences in TP53, KRAS, ERBB2 and MET by demographics, treatment and/or time. Clinical trial information: NCT02411448 .

Epidemiology and mortality of metastatic castration-resistant prostate cancer (mCRPC) in a managed care population in the United States.

e13592 Background: To date, there has been a paucity of information in the literature describing the epidemiology of mCRPC within the prostate cancer population. We present a real-world data study describing characteristics and mortality of patients with mCRPC within an administrative claims database of an insured population within the United States. Methods: In an administrative claims database of ≈18,000,000 covered lives, adult male patients were included if they had ≥1 claim for prostate cancer (ICD-9: 185 or 233.4; ICD-10: C61 or D075), underwent pharmacologic or surgical castration, and had a code for metastatic disease during the identification period (January 1, 2008–March 31, 2018). The index date was the first metastatic claim; 6 months of continuous enrollment (CE) prior to (baseline period) and after (follow-up period) the index date was required. Patients with metastatic claims in the baseline period were excluded. Patients were followed until the earliest of: death (unless prior to the 6-month CE), end of study period, or disenrollment. A claims-based algorithm was employed to identify locally advanced and distant mCRPC patients in the prostate cancer study population. Mortality data were sourced from the Social Security Administration Medicare data, and a claims algorithm. Results: 343,089 patients were identified with a claim for prostate cancer; of those, 3690 mCRPC cases (1.1%) were identified using the claims-based algorithm and met the study inclusion criteria. Median age was 75 years. Insurance type included commercial plans (27%) and Medicare (73%). Castration type included pharmacologic (99%) and surgical (1%). First claims for metastases were most commonly in the bone (65%) or lymph nodes (15%), with 20% in other sites. The study population averaged a Charlson comorbidity index score of 3.05 at baseline, with 16% of patients receiving a score of ≥5. The most common baseline comorbidities were hypertension (67%), urinary disease (58%), dyslipidemia (52%), and cardiac disease (45%). Median follow-up time among the mCRPC group was 538 days, during which 1834 deaths occurred; 50% of the population experienced mortality during the study period. Conclusions: This study provides valuable insights into the epidemiology, clinical characteristics, prevalence rate, and mortality of patients with mCRPC. Given the high mortality proportion of this disease, the development of novel therapies to prolong life in patients with mCRPC is warranted.

In-depth comparison of deep artificial neural network architectures on seismic events classification

One of the most challenging tasks in volcanic data analysis is the classification of seismic events. By knowing them, it is possible to take decisions in advance, providing benefits for the neighboring societies as, for instance, how such events may impact users' life and cropland areas. Although there are several approaches to perform such task, Deep Neural Networks (DNN) have been barely considered to deal with seismic signals, as discussed in our related work. In this sense, we started our research with a wide set of experiments to analyze the DNN performance while discriminating seismic activities through two common network architectures: a 2D Convolutional Neural Networks (CNN) and the Long Short-Term Memory (LSTM) network. In attempt to draw a parallel of DNN and classical supervised learning strategies, we extended our study by comparing those two architectures against the Multilayer Perceptron (MLP), which would be the simplest and most common baseline to take into account. Our MLP network was implemented after combining a basic architecture with elements from DNN models as dropout, flatten, and batch normalization layers. From this first analysis, we confirmed the need of additional neural network layers to obtain good classification results for seismic events, given the MLP required more DNN-based pipeline operations to improve its overall performance, making it comparable to the previous results obtained by our research group. As a natural extension, we designed a new DNN architecture by assessing additional network layers specifically devoted to extract features from seismic signals, thus improving the overall classification of Volcano activities involving the following events: Volcano-Tectonic (VT), Long Period (LP), Tremor (TR) and Tectonic (TC). Such design motivated the study of current DNN architectures tackling similar problems to classify raw signals from which SoundNet was taken as the most prominent candidate, thus leading to a new CNN architecture here proposed and referred to as SeismicNet. As our final contribution, SeismicNet provided classification results among the best in the literature without demanding explicit signal pre-processing steps though.

Remarkably high prevalence of overuse-related knee complaints and MRI abnormalities in youth competitive alpine skiers: a descriptive investigation in 108 athletes aged 13–15 years

Background/aimCompetitive alpine skiing is known to be associated with a high risk of traumatic knee injuries. However, little is known about the exact prevalence of knee overuse injuries and their...

Dop‐NET: a micro‐Doppler radar data challenge

Radar sensors have a new growing application area of dynamic hand gesture recognition. Traditionally radar systems are considered to be very large, complex and focused on detecting targets at long ranges. With modern electronics and signal processing it is now possible to create small compact RF sensors that can sense subtle movements over short ranges. For such applications, access to comprehensive databases of signatures is critical to enable the effective training of classification algorithms and to provide a common baseline for benchmarking purposes. This Letter introduces the Dop-NET radar micro-Doppler database and data challenge to the radar and machine learning communities. Dop-NET is a database of radar micro-Doppler signatures that are shareable and distributed with the purpose of improving micro-Doppler classification techniques. A continuous wave 24 GHz radar module is used to capture the first contributions to the Dop-NET database and classification results based on discriminating these hand gestures as shown.

Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach.

BackgroundBipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days.ObjectiveThis study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial.MethodsWe applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood for up to seven days based on 15,975 smartphone self-assessments from 84 patients with bipolar disorder participating in a randomized clinical trial. We reported the results of two time-series cross-validation 1-day forecast experiments corresponding to two different real-world scenarios and compared the outcomes with commonly used baseline methods. We then applied the best model to evaluate a 7-day forecast.ResultsThe best performing model used a history of 4 days of self-assessment to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a 1-day forecast time-series cross-validation experiment and achieved the predicted metrics, R2=0.51 and root mean squared error of 0.32, for mood scores on a scale of −3 to 3. When increasing the forecast horizon, forecast errors also increased and the forecast regressed toward the mean of data distribution.ConclusionsOur proposed method can forecast mood for several days with low error compared with common baseline methods. The applicability of a mood forecast in the clinical treatment of bipolar disorder has also been discussed.

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

AbstractStochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some existing stochastic parameterizations utilize data‐driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and subgrid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate time scales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both time scales, and the networks closely reproduce the spatiotemporal correlations and regimes of the Lorenz '96 system. We also find that, in general, those models which produce skillful forecasts are also associated with the best climate simulations.

Epidermal growth factor receptor mutation status in pulmonary adenocarcinoma: Multi-institutional data discussion at national conference of “Lung Cancer Management in Indian context”

The presence of activating mutations in the tyrosine kinase domain of the epidermal growth factor receptor (EGFR) gene in adenocarcinomas of lung confers sensitivity to tyrosine kinase inhibitor therapy. The prevalence of EGFR mutations varies among different ethnicities and demographic profile. This multi-institutional data was compiled to determine the EGFR mutation status in Indians patients with lung adenocarcinoma. Cohorts represented by 4 tertiary care hospitals participated in data discussion at a national conference entitled as ‘Lung Cancer Management in Indian Context’. The clinicopathologic data and EGFR mutation rate in the patients of lung adenocarcinoma screened in these cohorts were collected and analyzed. The sample types included both surgical and cytological specimens. A variety of methods were used including immunohistochemistry, polymerase chain reaction, Sanger sequencing and next generation sequencing. A total of 3436 cases of treatment naïve lung adenocarcinoma were tested for EGFR mutations. The overall frequency of EGFR mutations observed was 30.03%. The most common baseline mutation detected was exon 19 deletion followed by L858R point mutation in exon 21. Dual mutations were observed in 6.5% of cases and were predominantly combinations of exon 19 deletion and T790M point mutation in exon 20. Incidence of EGFR mutations was higher among females and non-smokers diagnosed with lung adenocarcinomas. The most common histology in EGFR mutant cases was acinar predominant adenocarcinomas. With nearly one-third of Indian patients with lung adenocarcinoma harboring EGFR mutations, routine testing for these mutations is important to get the benefit of targeted therapy.

Regression Models and Multivariate Life Tables

Semiparametric, multiplicative-form regression models are specified for marginal single and double failure hazard rates for the regression analysis of multivariate failure time data. Cox-type estimating functions are specified for single and double failure hazard ratio parameter estimation, and corresponding Aalen–Breslow estimators are specified for baseline hazard rates. Generalization to allow classification of failure times into a smaller set of failure types, with failures of the same type having common baseline hazard functions, is also included. Asymptotic distribution theory arises by generalization of the marginal single failure hazard rate estimation results of Lin et al. The Péano series representation for the bivariate survival function in terms of corresponding marginal single and double failure hazard rates leads to novel estimators for pairwise bivariate survival functions and pairwise dependency functions, at specified covariate history. Related asymptotic distribution theory follows from that for the marginal single and double failure hazard rates and the continuity, compact differentiability of the Péano series transformation and bootstrap applicability. Simulation evaluation of the proposed estimation procedures is presented, and an application to multiple clinical outcomes in the Women’s Health Initiative Dietary Modification Trial is provided. Higher dimensional marginal hazard rate regression modeling is briefly mentioned. Supplementary materials for this article are available online.

The global risk continuum (GRC)

Space-related hazards come from both natural and man-made sources; these hazards pose risks based on the likelihood of exposure and consequence of that exposure. The use of satellites in Earth orbit is now essential to the functioning of nation-states and commerce. The loss of these resources increases global risk as both the world population and global reliance on technology increase. Risks from natural cosmic threats from outside the confines of Earth include asteroids (i.e., Near Earth Objects, NEOs), solar flares, and coronal mass ejections (CMEs). Threats from Near-Earth Objects (NEOS) are becoming clearer as our ability to detect and characterize them improves. Space weather, driven largely by solar dynamics, and changes to the Earth's magnetic field that could decrease the natural protective shielding of Earth against solar storms, likewise threaten our planet.At the same time, the risk to the world's population from catastrophes originating from human activity, war, pandemics, geophysical phenomena, weather, and other Earth-based risks appear to be increasing. As the global population has increased from 800 million in 1800 to nearly eight billion in 2019, the number of people whose lives might be affected by these low probability and high consequence events has risen significantly.While it is difficult to precisely determine the effect of any of these risks individually, it can be instructive to create a common framework that allows comparison of a diverse set of hazards to evaluate risk awareness, mitigation, and remediation. These hazards span from high probability, moderate consequence events to low probability high consequence scenarios.The Global Risk Continuum (GRC) presents individual scenarios within these threat classes along a single risk scale. As a first step in this analysis, a GRC is created by independently determining the probability for each type of event, and the consequence of each as represented in an estimated economic impact. The risk is then determined by taking the product of these two terms. Assumptions and conversion factors needed to enable this common baseline and preliminary observations of the results are provided.This is a "work in progress" that will require significant cooperation and collaboration to refine over time into a useful tool for policymakers, scientists, and others. The process of establishing 'estimates' of risk is an iterative process that will evolve and improve over time. This paper describes a thought process and the beginnings of a methodology rather than a complete tool for global risk assessment.

Musical Emotion Recognition with Spectral Feature Extraction based on a Sinusoidal Model with Model-based and Deep-learning approaches.

This paper presents a method for extracting novel spectral features based on a sinusoidal model. The method is focused on characterizing the spectral shapes of audio signals using spectral peaks in frequency sub-bands. The extracted features are evaluated for predicting the levels of emotional dimensions, namely arousal and valence. Principal component regression, partial least squares regression, and deep convolutional neural network (CNN) models are used as prediction models for the levels of the emotional dimensions. The experimental results indicate that the proposed features include additional spectral information that common baseline features may not include. Since the quality of audio signals, especially timbre, plays a major role in affecting the perception of emotional valence in music, the inclusion of the presented features will contribute to decreasing the prediction error rate.

Long-term Outcome of Synthetic Mesh Use in Iranian Women with Genital Prolapse.

To evaluate the long-term outcome of synthetic mesh use in the treatment of women with Pelvic organ prolapse (POP). We evaluated the outcome of synthetic mesh implantation by vaginal surgery method in 153 women (mean age of 53.66±9.31 years) with POP grade >2 in the anterior compartment. Demographic findings, baseline symptoms as well as subjective and objective outcome were recorded during the follow-up period of 36.89±11.33 months. Results: POP relapse occurred in 3.3% indicative of 96.7% anatomical success rate. Patients' common baseline findings were frequency (72.5%), stress and urge incontinence (59.5% and 47.7%). Subjective outcome were vaginal pain (13.7%), dyspareunia (9.2%) and tension feeling (8.5%), while objective outcomes were mesh exposure (3.9%), urge incontinence (11.1%) and vaginal infection (1.3%). Stress incontinence was completely treated following surgery. There was significant improvement in dyspareunia, vaginal pain, urge and stress incontinence (all p < 0.001) and fecal incontinence (p = 0.02). After surgery, 88.42% were satisfied of the surgery outcome. POP surgery with synthetic mesh has acceptable results, considerable improvement in symptoms and high rate of satisfaction during follow-up; however, side effects are not uncommon but tolerable.

An Improved Method with High Anti-interference Ability for R Peak Detection in Wearable Devices

The rapid development of the wearable electrocardiogram monitoring equipment increases the requirements for R peak detection in wearable devices. An improved method called ISC algorithm is proposed with high anti-interference ability for R peak detection in wearable devices based on a simple basic algorithm called SC algorithm. The proposed method is characterized by using the updated amplitude selection threshold, updated slope comparison threshold and RR interval judgement to reduce false positives and false negatives. For data from MIT-BIH Arrhythmia Database, the positive predictivity P+ of ISC algorithm can reach 99.12%, and the sensitivity Se of ISC algorithm is more than 95%. For MIT-BIH Noise Stress Test Database, the accuracy of ISC algorithm for both sensitivity Se and positive predictivity P+ can exceed 94% under three common noise, baseline wander, muscle artifact, and electrode motion artifact, where the positive predictivity P+ of ISC algorithm is 44.46% higher than that of SC algorithm on average. For wearable devices in exercise, even under the exercise intensity of 7 km per hour, the average positive predictivity P+ of ISC algorithm is 99.32%, which is 60.93% higher than that of SC algorithm. The high anti-interference ability shows that ISC algorithm is suitable for R peak detection in wearable devices.

Comparison of effectiveness of hold-relax technique and maitlandfs mobilization in improving range of motion in posttraumatic stiffness of knee joint

Aims: This study aims to compare the effectiveness of Hold-Relax technique and Maitland's Mobilization in improving range of motion (ROM) in posttraumatic stiffness of knee joint. Materials and Methods: This was a randomized interventional comparative study with 30 subjects with posttraumatic stiffness of knee joint with restricted range of knee flexion were randomly allotted into two groups: Group A was given Hold-Relax technique and Group B was given Maitland's Mobilization consisted of Grade 3 posterior glide along with paraffin wax bath and isometric exercises of quadriceps and hamstring as a common baseline treatment for 5 days per week for 4 weeks. The baseline outcome measure was active knee flexion ROM measured by universal Goniometer before and after 4 weeks interventions. Results: Statistical analysis of data was done using paired t-test and unpaired t-test. Paired t-test showed that statistically significant increase in active knee flexion ROM in both the groups. On comparing by unpaired t-test it was found that both the techniques are equally effective in improving ROM. Conclusion: Both the Hold-Relax technique and Maitland's Mobilization are equally effective, in improving the knee flexion ROM.

Enabling interprofessional collaboration in delirium management / Interprofessionelle Zusammenarbeit im Delir-Management gestalten

Abstract Introduction Interprofessional collaboration (IPC) is dependent on different expectations and communication styles. IPC is a meaningful approach to accomplish treatment goals, especially in patients with delirium. Delirium affects approximately 50 % of patients older than 65 years in acute care settings. The constant attention and effort needed to care for patients suffering from delirium is challenging and cannot be provided by one profession alone. Instead, there is a need for IPC. Aim This study aims to analyse the structure of IPC in the treatment of patients suffering from delirium. Method Data was collected by conducting three group interviews and six individual interviews with members of different professions in an acute care hospital, and analysed by Charmaz's (2014) grounded theory approach. Results A model called enabling IPC in delirium management was developed. This model shows how mutual respect and appreciation, being in dialog and dealing with challenges in IPC interact with each other and also affect each other. Mutual respect and appreciation are common baseline values that have a pivotal effect on the dialog between professions and the management of IPC challenges. Being in dialog and dealing with challenges in IPC are essential values for enabling IPC, as well as mutual respect and appreciation. Conclusion Mutual respect and appreciation is fundamental for enabling IPC in the treatment of patients suffering from delirium. Interprofessional education, structured interprofessional care conferences and standardised communication can offer opportunities to foster mutual respect and appreciation which, in turn enable IPC.

Reducing Interanalyst Variability in Photovoltaic Degradation Rate Assessments

The economic return on investment of a commercial photovoltaic system depends greatly on its performance over the long term and, hence, its degradation rate. Many methods have been proposed for assessing system degradation rates from outdoor performance data. However, comparing reported values from one analyst and research group to another requires a common baseline of performance; consistency between methods and analysts can be a challenge. An interlaboratory study was conducted involving different volunteer analysts reporting on the same photovoltaic performance data using different methodologies. Initial variability of the reported degradation rates was so high that analysts could not come to a consensus whether a system degraded or not. More consistent values are received when written guidance is provided to each analyst. Further improvements in analyst variance was accomplished by using the free open-source software RdTools, allowing a reduction in variance between analysts by more than two orders of magnitude over the first round, where multiple analysis methods are allowed. This article highlights many pitfalls in conducting “routine” degradation analysis, and it addresses some of the factors that must be considered when comparing degradation results reported by different analysts or methods.

Social cognitions and smoking behaviour: Temporal resolution matters.

Objectives Health behaviour theories outline how cognitions predict behaviours, but rarely specify the temporal relation between cognitions and behaviours. It is not known whether these predictive relationships vary depending on temporal resolution or whether the relative influence of cognitions varies with measurement schedules. The current exploratory study therefore investigates whether the associations between behavioural cognitions (self-efficacy, intention, and risk perception) and smoking vary when measured momentarily, at day level, or using the more common baseline-follow-up design. Design EMA study involving 36 continuing smokers over 17days. Participants logged cigarettes and reported their cognitions at baseline, daily (evening), and in response to momentary surveys. Methods Random-effects models were used to compare the effects of cognitions measured at different time points on (1) the number of cigarettes smoked daily and (2) the time interval until the next cigarette smoked. Results Self-efficacy and risk perception measured at baseline significantly predicted cigarettes smoked each day, but this effect became non-significant when daily measurements of cognitions were included in the model. Momentary smoking behaviour was predicted by momentary measurements of risk perception, with no significant effects of social cognitions at baseline. Conclusions Relationships between cognitions and behaviours vary according to the temporal resolution of the measurement schedule. Ensuring that the temporal resolution of assessment is appropriate for the temporal dynamics of the behaviour being assessed is important. Future research is needed to investigate the potential for leveraging specific cognitive processes depending on temporal importance in order to increase health-promoting behaviours. Statement of contribution What is already known on this subject? Social cognitions including intentions, risk perception, and self-efficacy have been observed to predict smoking. Little is known about the role of time in the cognition-behaviour relationship. Cognitions have been observed to fluctuate, but instability is typically not considered in research design. What does this study add? Daily measurement of social cognitions predicts behaviour better than measurements taken at baseline. Momentary smoking behaviour is predicted by momentary cognitions at the intra-individual level. Temporal resolution of measurement should be considered when investigating cognition-behaviour relationships.

Abstract PR07: Phase II Trial of the MEK 1/2 inhibitor selumetinib (AZD6344, ARRY-142886 hydrogen sulphate) in adults with neurofibromatosis type 1 (NF1) and inoperable plexiform neurofibromas (PN)

Abstract Background: Neurofibromatosis type 1 (NF1)-related plexiform neurofibromas (PN) are locally invasive tumors that can cause debilitating complications including pain, disfigurement, and functional limitations. The primary treatment option is surgical debulking, which is often challenging and can lead to recurrence. As these tumors demonstrate increased activation of the MAP kinase (MAPK) pathway, inhibition of MAPK kinase (MEK) represents a rational therapeutic strategy. Selumetinib, an oral MEK inhibitor, has recently received breakthrough designation for NF1-related PN based on two early phase studies for children with NF1 and inoperable PN showing unprecedented 71% and 74% response rates (RR). Here we report the initial results of an ongoing phase II study of selumetinib in adults with NF1 and inoperable PN, which includes pharmacodynamic (PD) evaluation of serial PN and cutaneous neurofibroma (cNF) biopsies, as well as patient-reported outcomes (PROs) (NCT02407405). Materials and Methods: Open-label Simon 2-stage single-site study in patients ≥18 years old with NF1, inoperable PN, and ≥1 PN-related morbidity; overall target RR of 45%. Selumetinib 50 mg is administered twice daily orally on a continuous dosing schedule (28-day cycles). The primary study objective is to determine the partial response (PR; ≥20% volume decrease) and complete response (CR) rate of PN using volumetric MRI analysis; secondary objectives include PD studies on mandatory pre- and on-treatment biopsies of PN and cNF, and assessment of clinical benefit using PROs (Numeric Rating Scale-11 and Pain Interference Index). Results: As of August, 2019, a total of 26 patients (pts) have been enrolled (69% male; median age 33 years, range 18-60). We report outcomes for 21 pts who to date have a 1-year minimum time on study. Eighteen pts had a typical PN target lesion on baseline imaging. The most common baseline PN-related morbidities were motor weakness (13 pts) and disfigurement (11 pts). Nineteen pts underwent serial photography. Fourteen pts (14/21; 67%) achieved a PR, with 11 pts confirmed on ≥2 consecutive restaging scans. Three pts had a biopsy-confirmed atypical neurofibroma (aNF) as a target lesion, of which 2 are PRs. Nineteen pts had percutaneous neurofibroma and/or cNF biopsy with confirmed neurofibroma pathology. The median change in PN volume at the time of best response was -22% (range -41% to +5.5%). Four pts were dose-reduced due to toxicity (rash=3 pts, transaminitis=1 pt); two of these pts required a second reduction in dose. Grade ≥3 drug-related toxicities included transaminitis (5 pts, 23%), rash (4 pts, 19%), and pancreatic enzyme elevation (4 pts, 19%). Between baseline and end of 1-year evaluation, patient-reported target tumor pain intensity and pain interference scores both have significantly improved (p&amp;lt;0.002). PD analysis continues, with baseline PN core biopsies and cNF shave biopsies delivering sufficient protein yield and phospho- ERK/phospho-MEK levels for PD target inhibition assessment. Twenty-one pts (80%) remain on study; 4 pts discontinued treatment (rash, surgical resection, patient choice, and non-compliance). Conclusion: Selumetinib shows significant objective responses and subjective clinical benefit in adult patients with symptomatic NF1-associated PN. PD marker analysis is ongoing, and stage 2 enrollment is continuing. Funded by NCI Contract No HHSN261200800001E. Citation Format: Geraldine O'Sullivan Coyne, Andrea Gross, Eva Dombi, Jennifer DeSanto, Amanda Carbonell, Joanne Derdak, Dominique Pichard, Brad Wood, Cecilia Tibery, Pam Wolters, Trish Whitcomb, Staci Martin, Diana Bradford, Apurva Srivastava, William Herrick, Ralph Parchment, Kara Heisey, Austin Doyle, Naoko Takebe, James H Doroshow, Alice P Chen, Brigitte Widemann. Phase II Trial of the MEK 1/2 inhibitor selumetinib (AZD6344, ARRY-142886 hydrogen sulphate) in adults with neurofibromatosis type 1 (NF1) and inoperable plexiform neurofibromas (PN) [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr PR07. doi:10.1158/1535-7163.TARG-19-PR07

Error analysis for lidar retrievals of atmospheric species from absorption spectra.

We generalize a model for retrieving atmospheric constituents from lidar absorption spectra measured at any laser frequency channels. Random and systematic retrieval errors from measurement noise and model bias, respectively, are analyzed parametrically and numerically to provide deeper insight. By placing four or more channels symmetrically around the absorption peak, retrieval errors from a common laser frequency shift and spectral baseline tilt can be eliminated. By solving for the frequency shift and spectral baseline tilt, atmospheric retrievals degrade only slightly even when such channels are shifted substantially out of symmetry. An etalon can thus be used for the wavelength stabilization.