Daily ozone cycle in the stratosphere: global, regional and seasonal behaviour modelled with the Whole Atmosphere Community Climate Model

The Whole Atmosphere Community Climate Model (WACCM) is utilised to study the daily ozone cycle and underlying photochemical and dynamical processes. The analysis is focused on the daily ozone cycle in the middle stratosphere at 5 hPa where satellite-based trend estimates of stratospheric ozone are most biased by diurnal sampling effects and drifting satellite orbits. The simulated ozone cycle shows a minimum after sunrise and a maximum in the late afternoon. Further, a seasonal variation of the daily ozone cycle in the stratosphere was found. Depending on season and latitude, the peak-to-valley difference of the daily ozone cycle varies mostly between 3 and 5% (0.4 ppmv) with respect to the midnight ozone volume mixing ratio. The maximal variation of 15% (0.8 ppmv) is found at the polar circle in summer. The global pattern of the strength of the daily ozone cycle is mainly governed by the solar zenith angle and the sunshine duration. In addition, we find synoptic-scale variations in the strength of the daily ozone cycle. These variations are often anti-correlated to regional temperature anomalies and are due to the temperature dependence of the rate coefficients k2 and k3 of the Chapman cycle reactions. Further, the NOx catalytic cycle counteracts the accumulation of ozone during daytime and leads to an anti-correlation between anomalies in NOx and the strength of the daily ozone cycle. Similarly, ozone recombines with atomic oxygen which leads to an anti-correlation between anomalies in ozone abundance and the strength of the daily ozone cycle. At higher latitudes, an increase of the westerly (easterly) wind cause a decrease (increase) in the sunshine duration of an air parcel leading to a weaker (stronger) daily ozone cycle.

Therapeutic drug monitoring of once daily aminoglycoside dosing: comparison of two methods and investigation of the optimal blood sampling strategy.

PURPOSE Therapeutic drug monitoring of patients receiving once daily aminoglycoside therapy can be performed using pharmacokinetic (PK) formulas or Bayesian calculations. While these methods produced comparable results, their performance has never been checked against full PK profiles. We performed a PK study in order to compare both methods and to determine the best time-points to estimate AUC0-24 and peak concentrations (C max). METHODS We obtained full PK profiles in 14 patients receiving a once daily aminoglycoside therapy. PK parameters were calculated with PKSolver using non-compartmental methods. The calculated PK parameters were then compared with parameters estimated using an algorithm based on two serum concentrations (two-point method) or the software TCIWorks (Bayesian method). RESULTS For tobramycin and gentamicin, AUC0-24 and C max could be reliably estimated using a first serum concentration obtained at 1 h and a second one between 8 and 10 h after start of the infusion. The two-point and the Bayesian method produced similar results. For amikacin, AUC0-24 could reliably be estimated by both methods. C max was underestimated by 10-20% by the two-point method and by up to 30% with a large variation by the Bayesian method. CONCLUSIONS The ideal time-points for therapeutic drug monitoring of once daily administered aminoglycosides are 1 h after start of a 30-min infusion for the first time-point and 8-10 h after start of the infusion for the second time-point. Duration of the infusion and accurate registration of the time-points of blood drawing are essential for obtaining precise predictions.

A web-based non-intrusive ambient system to measure and classify activities of daily living.

BACKGROUND The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). OBJECTIVE We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. METHODS The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. RESULTS In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75). CONCLUSIONS The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

Lack of association between dual antiplatelet therapy use and stent thrombosis between 1 and 12 months following resolute zotarolimus-eluting stent implantation.

AIM The optimal duration of dual antiplatelet therapy (DAPT) following the use of new generation drug-eluting stents is unknown. METHODS AND RESULTS The association between DAPT interruption and the rates of stent thrombosis (ST) and cardiac death/target-vessel myocardial infarction (CD/TVMI) in patients receiving a Resolute zotarolimus-eluting stent (R-ZES) was analysed in 4896 patients from the pooled RESOLUTE clinical programme. Daily acetylsalicylate (ASA) and a thienopyridine for 6-12 months were prescribed. A DAPT interruption was defined as any interruption of ASA and/or a thienopyridine of >1 day; long interruptions were >14 days. Three groups were analysed: no interruption, interruption during the first month, and >1-12 months. There were 1069 (21.83%) patients with a DAPT interruption and 3827 patients with no interruption. Among the 166 patients in the 1-month interruption group, 6 definite/probable ST events occurred (3.61%; all long DAPT interruptions), and among the 903 patients in the >1-12 months (60% occurred between 6 and 12 months) interruption group, 1 ST event occurred (0.11%; 2-day DAPT interruption). Among patients with no DAPT interruption, 32 ST events occurred (0.84%). Rates of CD/TVMI were 6.84% in the 1-month long interruption group, 1.41% in the >1-12 months long interruption group, and 4.08% in patients on continuous DAPT. CONCLUSION In a pooled population of patients receiving an R-ZES, DAPT interruptions within 1 month are associated with a high risk of adverse outcomes. Dual antiplatelet therapy interruptions between 1 and 12 months were associated with low rates of ST and adverse cardiac outcomes. Randomized clinical trials are needed to determine whether early temporary or permanent interruption of DAPT is truly safe. CLINICAL TRIALSGOV IDENTIFIERS NCT00617084; NCT00726453; NCT00752128; NCT00927940.

Volitional processes and daily smoking: Examining inter- and intraindividual associations around a quit attempt

The Health Action Process Approach (HAPA) assumes that volitional processes are important for effective behavioral change. However, intraindividual associations have not yet been tested in the context of smoking cessation. This study examined the inter- and intraindividual associations between volitional HAPA variables and daily smoking before and after a quit attempt. Overall, 100 smokers completed daily surveys on mobile phones from 10 days before until 21 days after a self-set quit date, including self-efficacy, action planning, action control, and numbers of cigarettes smoked. Negative associations between volitional variables and daily numbers of cigarettes smoked emerged at the inter- and intraindividual level. Except for interindividual action planning, associations were stronger after the quit date than before the quit date. Self-efficacy, planning and action control were identified as critical inter- and intraindividual processes in smoking cessation, particularly after a self-set quit attempt when actual behavior change is performed.

Invisible social control as predictor of daily negative affect and smoking after a self-set quit date

The dual-effects model of social control proposes that social control leads to increased psychological distress but also to better health practices. However, findings are inconsistent, and recent research suggests that the most effective control is unnoticed by the receiver (i. e., invisible). Yet, investigations of the influence of invisible control on daily negative affect and smoking have been limited. Using daily diaries, we investigated how invisible social control was associated with negative affect and smoking. Overall, 100 smokers (72.0 % men, age M = 40.48, SD = 9.82) and their nonsmoking partners completed electronic diaries from a self-set quit date for 22 consecutive days, reporting received and provided social control, negative affect, and daily smoking. We found in multilevel analyses of the within-person process that on days with higher-than-average invisible control, smokers reported more negative affect and fewer cigarettes smoked. Findings are in line with the assumptions of the dual-effects model of social control: Invisible social control increased daily negative affect and simultaneously reduced smoking at the within-person level.

Daily negative affect and smoking after a self-set quit attempt: The role of dyadic invisible social support in a daily diary study

Objectives Social support receipt from one's partner is assumed to be beneficial for successful smoking cessation. However, support receipt can have costs. Recent research suggests that the most effective support is unnoticed by the receiver (i.e., invisible). Therefore, this study examined the association between everyday levels of dyadic invisible emotional and instrumental support, daily negative affect, and daily smoking after a self-set quit attempt in smoker–non-smoker couples. Methods Overall, 100 smokers (72.0% men, mean age M = 40.48, SD = 9.82) and their non-smoking partners completed electronic diaries from a self-set quit date on for 22 consecutive days, reporting daily invisible emotional and instrumental social support, daily negative affect, and daily smoking. Results Same-day multilevel analyses showed that at the between-person level, higher individual mean levels of invisible emotional and instrumental support were associated with less daily negative affect. In contrast to our assumption, more receipt of invisible emotional and instrumental support was related to more daily cigarettes smoked. Conclusions The findings are in line with previous results, indicating invisible support to have beneficial relations with affect. However, results emphasize the need for further prospective daily diary approaches for understanding the dynamics of invisible support on smoking cessation.

Examining invisible social control as predictor of daily smoking and negative affect after a self-set quit date

Objectives: The dual-effects model of social control proposes that social control leads to better health practices, but also arouses psychological distress. However, findings are inconsistent in relation to health behavior and psychological distress. Recent research suggests that the most effective control is unnoticed by the receiver (i.e., invisible). There is some evidence that invisible social control is beneficial for positive and negative affective reactions. Yet, investigations of the influence of invisible social control on daily smoking and distress have been limited. In daily diaries, we investigated how invisible social control is associated with number of cigarettes smoked and negative affect on a daily basis. Methods: Overall, 99 smokers (72.0% men, mean age M = 40.48, SD = 9.82) and their non-smoking partners completed electronic diaries from a self-set quit date for 22 consecutive days within the hour before going to bed, reporting received and provided social control, daily number of cigarettes smoked, and negative affect. Results: Multilevel analyses indicated that between-person levels of invisible social control were associated with lower negative affect, whereas they were unrelated to number of cigarettes smoked. On days with higher-than-average invisible social control, smokers reported less cigarettes smoked and more negative affect. Conclusions: Between-person level findings indicate that invisible social control can be beneficial for negative affect. However, findings on the within-person level are in line with the assumptions of the dual-effects model of social control: Invisible social control reduced daily smoking and simultaneously increased daily negative affect within person.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.