Daily Measurements Research Articles

An open-label feasibility trial of transdermal cannabidiol for hand osteoarthritis

Hand osteoarthritis (OA) is an irreversible degenerative condition causing chronic pain and impaired functionality. Existing treatment options are often inadequate. Cannabidiol (CBD) has demonstrated analgesic and anti-inflammatory effects in preclinical models of arthritis. In this open-label feasibility trial, participants with symptomatically active hand OA applied a novel transdermal CBD gel (4% w/w) three times a day for four weeks to their most painful hand. Changes in daily self-reported pain scores were measured on a 0–10 Numeric Pain Rating Scale (NPRS). Hand functionality was determined via daily grip strength measures using a Bluetooth equipped squeeze ball and self-report questionnaire. Quality of life (QoL) ratings around sleep, anxiety, stiffness and fatigue were also measured. All self-report measures and grip strength data were gathered via smartphone application. Urinalysis was conducted at trial end to determine systemic absorption of CBD. Eighteen participants were consented and 15 completed the trial. Pain ratings were significantly reduced over time from pre-treatment baseline including current pain (− 1.91 ± 0.35, p < 0.0001), average pain (− 1.92 ± 0.35, p < 0.0001) and maximum pain (− 1.97 ± 0.34, p < 0.0001) (data represent mean reduction on a 0–10 NPRS scale ± standard error of the mean (SEM)). A significant increase in grip strength in the treated hand (p < 0.0001) was observed although self-reported functionality did not improve. There were significant (p < 0.005) improvements in three QoL measures: fatigue, stiffness and anxiety. CBD and its metabolites were detected at low concentrations in all urine samples. Measured reductions in pain and increases in grip strength seen during treatment reverted back towards baseline during the washout phase. In summary, pain, grip strength and QoL measures, using smartphone technology, was shown to improve over time following transdermal CBD application suggesting feasibility of this intervention in relieving osteoarthritic hand pain. Proof of efficacy, however, requires further confirmation in a placebo-controlled randomised trial.Trial registration: ANZCTR public trials registry (ACTRN12621001512819, 05/11/2021).

#2784 Deep learning algorithm predicts individual ultrafiltration trajectories for patients on automated peritoneal dialysis 12 weeks ahead

Abstract Background and Aims Adequate small solute clearance and ultrafiltration (UF) are the main requirements for fluid and salt homeostasis on peritoneal dialysis (PD). New automated peritoneal dialysis (APD) systems that allow remote patient monitoring in terms of PD treatment analysis have the potential to improve clinical outcomes. UF on APD although dependent on PD prescription, i.e. fill volumes and glucose concentration, exhibits substantial intra- and interindividual variation, even day-by-day. As conventional statistical methods are not able to assess these complex non-linear relationships adequately, we aimed to build a deep learning algorithm to predict ultrafiltration trajectories of individual patients 12 weeks ahead. Method Data on daily UF and PD prescriptions (i.e., glucose concentration, fill volumes) was extracted from the APD cycler management software (PD Link, Baxter, IL, USA) and patient charts at the Medical University of Vienna, for a secondary analysis of this cohort. The Keras high-level API for TensorFlow in R software (R Core Team 2023) was used to create a Long Short-Term Memory (LSTM) recurrent neural network based on historic prescription and gcUF data of 26 weeks (after the first 90 days of APD) to predict individual UF trajectories for the following 12 weeks. Results APD machine readouts from a total of 123 patients, with a mean time on PD of 117 (±73) days, were retrieved. The final recurrent neural network consisted of four LSTM layers with 64 cells each, sigmoid activation. The final model was fit using 30 epochs, 49 steps per epoch, and recurrent dropout to avoid overfitting. A 40-40-20% data split was used to train, validate, and test the model adequately. This deep learning algorithm was able to predict individual UF values, for each day of the upcoming 12 weeks, with an accuracy of at mean 201ml. Upon visual inspection, predicted individual UF trajectories smoothly resemble the overall day-by-day trajectory of the patient but fail to account for extreme spikes in either direction. Conclusion In conclusion, historic daily APD cycler and prescription data in coalescence with respective daily measurements of membrane function (i.e., gcUF) can be used to predict individual daily ultrafiltration values and trajectories of APD patients 12 weeks ahead using a deep learning algorithm. Therefore, continuous collection of PD prescription data and UF measurements may be used to monitor peritoneal membrane function of patients on APD and alert clinical teams upon detection of relevant changes of UF trajectories already 12 weeks ahead to facilitate early intervention. In contrast to current “screening” methods on PD, i.e., Peritoneal Equilibrium Test, these methods could reduce treatment burden.

#2160 Ketosis moderates the effect on kidney volume in dietary interventions for ADPKD—more insights on the KETO ADPKD trial

Abstract Background and Aims Autosomal-dominant polycystic kidney disease (ADPKD) is the most common monogenic disease leading to kidney failure. Tolvaptan, the only approved targeted treatment strategy, comes with adverse events such as hepatotoxicity and massive polyuria, limiting its use. Novel treatment strategies are urgently needed. Cyst-lining epithelial cells are glucose-dependent and metabolically inflexible. Evidence from polycystic kidney disease (PKD) animal models show that ketogenic dietary interventions (KDI) can ameliorate cyst growth and loss of kidney function. To enable clinical translation of these findings, our group set up a series of trials—from small cohorts and proof of principle studies to our most recent trial KETO-ADPKD, showing that KDIs are feasible and can work as a treatment for ADPKD [1]. This has received a lot of attention. With this post-hoc analyses, we aim to share further in-depth analyses of the factors moderating the effects we see on ADPKD. Method KETO-ADPKD is an exploratory randomized and controlled clinical trial (NCT04680780). Sixty-six patients were randomized to 3 months dietary intervention (ketogenic diet [KD] or water fasting [WF]) or the control group. Here, we explore correlations between biochemical readout parameters of ketosis and markers of disease progression. Results The KD group shows a promising, yet statistically not significant decline in height-adjusted total kidney volume (htTKV). Patients reaching high biochemical thresholds of ketosis however significantly reduced their htTKV in comparison with the control group (KD −16.3 ml/m, CG + 14.8 ml/m, p-value 0.049). This becomes even clearer when higher thresholds for adherence are administered: In a smaller group requiring not only beta-hydroxybutyrate (BHB) levels ≥0.5 mmol/l but breath acetone ≥5 ppm on 75% of daily measurements, htTKV could be reduced by −17.6 ml/m (KD) vs +14.8 ml/m (CG), p-value 0.026. The significant reduction of liver volume upon KD is not influenced by the level of ketosis. Beneficial effects on estimated glomerular filtration rate (eGFR) can be equally observed in all subsets. Weight loss ≥5% was nor associated with a more significant loss of kidney nor liver volume. Conclusion Subgroup analyses of the KETO-ADPKD trial show stronger impact of the dietary intervention with higher ketone body levels. In particular, ketogenesis as a marker of metabolic reprogramming strongly moderates the effects we see on kidney volume. The assessment of renal cyst fractions could further enlighten the effects on cyst burden. This is in line with preclinical data showing that ketosis rather than caloric intake is responsible for the amelioration of disease progression [2].

Dietary Caffeine and Brain Dopaminergic Function in Parkinson Disease.

This study was undertaken to investigate the effects of dietary caffeine intake on striatal dopamine function and clinical symptoms in Parkinson disease in a cross-sectional and longitudinal setting. One hundred sixty-three early Parkinson disease patients and 40 healthy controls were investigated with [123I]FP-CIT single photon emission computed tomography, and striatal dopamine transporter binding was evaluated in association with the level of daily coffee consumption and clinical measures. After a median interval of 6.1 years, 44 patients with various caffeine consumption levels underwent clinical and imaging reexamination including blood caffeine metabolite profiling. Unmedicated early Parkinson disease patients with high coffee consumption had 8.3 to 15.4% lower dopamine transporter binding in all studied striatal regions than low consumers, after accounting for age, sex, and motor symptom severity. Higher caffeine consumption was further associated with a progressive decline in striatal binding over time. No significant effects of caffeine on motor function were observed. Blood analyses demonstrated a positive correlation between caffeine metabolites after recent caffeine intake and dopamine transporter binding in the ipsilateral putamen. Chronic caffeine intake prompts compensatory and cumulative dopamine transporter downregulation, consistent with caffeine's reported risk reduction in Parkinson disease. However, this decline does not manifest in symptom changes. Transiently increased dopamine transporter binding after recent caffeine intake has implications for dopaminergic imaging guidelines. ANN NEUROL 2024;96:262-275.

Daily weight monitoring for inpatients receiving chemotherapy.

Unlike intake and output documentation, which is often inaccurate and inconsistent, daily weight measurement is a reliable method to assess fluid volume status. Daily weight assessment and monitoring are crucial for preventing volume overload in patients receiving chemotherapy in the inpatient setting.

How three-dimensional forest structure regulates the amount and timing of snowmelt across a climatic gradient of snow persistence

Across the western United States, forests are changing rapidly, with uncertain impacts on snowmelt water resources. Snow partitioning is controlled by forest effects on interception, radiation, and sublimation. Yet, models often lack snow measurements with sufficiently high spatial and temporal resolution across gradients of forest structure to accurately represent these fine-scale processes. Here, we utilize four Snowtography stations in Arizona, in the lower Colorado River Basin, with daily measurements over 3–5 years at ~110 positions distributed across gradients of forest structure resulting from wildfires and mechanical thinning. We combine Snowtography with lidar snapshots of forest and snow to train a high-resolution snow model and run it for 6 years to quantify how forest structure regulates snowpack and snowmelt. These study sites represent a climate gradient from lower/warmer ephemeral snowpack (~2,100 m asl) to higher/colder seasonal snowpack (~2,800 m asl). Forest cover reduced snowpack and snowmelt through canopy sublimation. Forest advanced snowmelt timing at lower/warmer sites but delayed it at higher/colder sites. Within canopy gaps, shaded cool edges had the greatest peak snow water equivalent (SWE). Surprisingly, sunny/warm gap edges produced more snowmelt than cool edges, because high radiation melted snow quickly, reducing exposure to sublimation. Therefore, peak SWE is not an ideal proxy for snowmelt volume from ephemeral snowpacks, which are becoming more prevalent due to warming. The results imply that forest management can influence the amount and timing of snowmelt, and that there may be decision trade-offs between enhancing forest resilience through delayed snowmelt and maximizing snowmelt volumes for downstream water resources.

Accurate Blood Pressure Measurement Using Smartphone's Built-in Accelerometer

Efficient blood pressure (BP) monitoring in everyday contexts stands as a substantial public health challenge that has garnered considerable attention from both industry and academia. Commercial mobile phones have emerged as a promising tool for BP measurement, benefitting from their widespread popularity, portability, and ease of use. Most mobile phone-based systems leverage a combination of the built-in camera and LED to capture photoplethysmography (PPG) signals, which can be used to infer BP by analyzing the blood flow characteristics. However, due to low Signal-to-Noise (SNR), various factors such as finger motion, improper finger placement, skin tattoos, or fluctuations in environmental lighting can distort the PPG signal. These distortions consequentially affect the performance of BP estimation. In this paper, we introduce a novel sensing system that utilizes the built-in accelerometer of a mobile phone to capture seismocardiography (SCG) signals, enabling accurate BP measurement. Our system surpasses previous mobile phone-based BP measurement systems, offering advantages such as high SNR, ease of use, and power efficiency. We propose a triple-stage noise reduction scheme, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), recursive least squares (RLS) adaptive filter, and soft-thresholding, to effectively reconstruct high-quality heartbeat waveforms from initially contaminated raw SCG signals. Moreover, we introduce a data augmentation technique encompassing normalization coupled with temporal-sliding, effectively augmenting the diversity of the training sample set. To enable battery efficiency on smartphone, we propose a resource-efficient deep learning model that incorporates resource-efficient convolution, shortcut connections, and Huber loss. We conduct extensive experiments with 70 volunteers, comprising 35 healthy individuals and 35 individuals diagnosed with hypertension, under a user-independent setting. The excellent performance of our system demonstrates its capacity for robust and accurate daily BP measurement.

Poor-Quality Daily Social Encounters, Daily Stress, and Subjective Cognitive Decline Among Older Adults.

Although prior research has shown that social relationships and daily stress are strongly associated with cognitive function, few studies have explored the link between the quality of daily social encounters and subjective cognitive decline (SCD). The present study explores whether the quality of older adults' daily social encounters is associated with SCD through daily stress. This study used data from 254 adults aged 70 or older (M age = 76.5 years, SD = 4.4; 67.7% women) who completed the Einstein Aging Study, a 2-week experience sampling study. Multilevel mediation analyses were conducted to account for daily measurements nested within individuals. We tested the indirect effect of the quality of daily social encounters on SCD through daily stress levels. There was a significant positive association between ambivalent and neutral social encounters and daily stress levels at both the within- and between-person levels. Between-person daily stress was, in turn, associated with greater SCD. Specifically, there was a significant indirect path from ambivalent social encounters to SCD through daily stress. This study contributes to a more detailed understanding of how the quality of daily social encounters can influence cognition via increased exposure to daily stress. The findings suggest that emotional support may be crucial to preserving perceptions of older adults' cognitive functioning.

Inadequate weight gain and factors influencing it among preterm neonates in neonatal intensive care units in the Amhara region, Ethiopia, in 2022.

Adequate weight gain is crucial for the health and development of preterm neonates admitted to neonatal intensive care units (NICUs). Understanding the factors influencing weight gain in this vulnerable population is essential for improving outcomes. This study aimed to assess the weight gain status and associated factors among preterm neonates admitted to NICUs in specialized hospitals in the Amhara region of Ethiopia. A cross-sectional study design involving 363 preterm neonates admitted to NICUs in specialized hospitals within the Amhara region was used. Data were collected using structured questionnaires and the Kobo Tool Box. Daily weight measurements were recorded for three consecutive days. Descriptive statistics, logistic regression analysis, and graphical presentations were utilized for data analysis and presentation. The study revealed that a significant proportion (80.8%) of preterm neonates experienced poor weight gain during their NICU stay. The factors significantly associated with poor weight gain were older maternal age, delayed initiation of enteral feeding, lack of kangaroo mother care (KMC), and inadequate antenatal care visits. Addressing the identified factors, such as providing adequate support during the antenatal period, promoting a timely initiation of enteral feeding, and encouraging KMC practices, is crucial for improving weight gain outcomes in preterm neonates. The findings highlight the importance of a comprehensive approach to neonatal care targeting both maternal and neonatal factors. Policymakers and healthcare providers should prioritize interventions aimed at optimizing weight gain in preterm neonates to improve these neonates' overall well-being and long-term outcomes.

222 Effect of electronically controlled floor cooling pad on thermoregulatory performance of Duroc boars during the summer heat stress period

Abstract The objective of the study was to evaluate the effects of using electronically controlled floor cooling pad (ECP) to reduce physiological heat stress response during the summer season in a boar stud in Scottsville, Kentucky. Boars (n = 60; 526 ± 188 d of age) were placed in two rows of pens (2.06 x 0.66 m) of a 350 head tunnel ventilated boar barn and were randomly assigned to either CONTROL (n = 30; no ECP) or ECP treatments (n = 30). The ECP had 4.25 L cold water flushed through a group of 5 pads connected to each other, when temperature sensors reached 27°C or every 4 min for sensors placed on 5th pad and 24°C or every 4 min for sensors placed on 1st pad. Boars experienced a natural summer heat stress environment (daily temperature, humidity and dew point: mean = 24.03°C, 87.9%, 21.7°C, hourly mean high = 28.9°C, 83.4%, 25.8°C) for a period of 30 d from July 15, 2023 to August 14, 2023 wherein the cooling pad remained on and were flushing based on the set points mentioned above. Daily physiological measurements included respiration rate (RR), rectal temp (RT) and feed refusal (FR) collected during the hottest part of the day (1300 to1600 h) via counting chest or belly rises, using rectal thermometer and visual estimation, respectively. A mixed model in SAS (v.9.4) including the fixed effects of ECP, week, day, boar ID and their interactions with day in the week included as a random effect was used for the statistical analysis. Both RR and RT were decreased by ECP (P &amp;lt; 0.001), week (P = 0.027 and P = 0.005, respectively) and treatment by week interactions (P &amp;lt; 0.001). The overall RR and RT for boars on ECP (Mean = 15.2 breaths/minute, 38°C) were more stable compared with Control (Mean = 43.4 breaths/minute, 38.4°C). Increased RR (Mean = 55.7 breaths/min; daily high = 164 breaths/minute) and RT (Mean = 38.7°C; daily high = 40°C) were noted in wk 30 and 33 for CONTROL group while ECP group remained stable. The wk 30 and 33 RR and RT are caused by increased environmental temperatures and humidity (hourly mean high: temperature = 28.9°C, humidity = 84.9%, dewpoint = 21°C). FR increased as barn temperatures increased and treatment by week interactions were significant (P &amp;lt; 0.001), with a tendency for ECP to be overall less than CONTROL at P = 0.064. CONTROL boars had greater FR than ECP boars for wk 31 (P &amp;lt; 0.001), 32 (P = 0.001) and tendency for wk 33 (P = 0.098). These results indicate that the ECP effectively reduced the RR, RT and FR across the weeks with greater effects seen during the hottest weeks in the 30-d trial.

Feasibility of self-measurement telemonitoring using a handheld heart sound recorder in patients with heart failure – SELPH multicenter pilot study

BackgroundMulti-parametric assessment, including heart sounds in addition to conventional parameters, may enhance the efficacy of noninvasive telemonitoring for heart failure (HF). We sought to assess the feasibility of self-telemonitoring with multiple devices including a handheld heart sound recorder and its association with clinical events in patients with HF. MethodsAmbulatory HF patients recorded their own heart sounds, mono‑lead electrocardiograms, oxygen saturation, body weight, and vital signs using multiple devices every morning for six months. ResultsIn the 77 patients enrolled (63 ± 13 years old, 84 % male), daily measurements were feasible with a self-measurement rate of >70 % of days in 75 % of patients. Younger age and higher Minnesota Living with Heart Failure Questionnaire scores were independently associated with lower adherence (p = 0.002 and 0.027, respectively). A usability questionnaire showed that 87 % of patients felt self-telemonitoring was helpful, and 96 % could use the devices without routine cohabitant support. Six patients experienced ten HF events of re-hospitalization and/or unplanned hospital visits due to HF. In patients who experienced HF events, a significant increase in heart rate and diastolic blood pressure and a decrease in the time interval from Q wave onset to the second heart sound were observed 7 days before the events compared with those without HF events. ConclusionsSelf-telemonitoring with multiple devices including a handheld heart sound recorder was feasible even in elderly patients with HF. This intervention may confer a sense of relief to patients and enable monitoring of physiological parameters that could be valuable in detecting the deterioration of HF.

Thermal efficiency and performance analysis of 50 MW concentrated solar power plant

This study evaluates the operational efficiency and performance of the Shagaya 50 MW Concentrated Solar Power (CSP) plant in Kuwait that has been operational since February 2019. Utilizing Parabolic Trough technology, the plant incorporates a large Solar Field (SF) comprising 8 platforms with total of 206 solar-collector loops. Thermal energy captured by the SF is utilized in a Low Temperature Rankine Cycle to generate electricity via water-steam cycle and heat exchangers, with a nominal turbine capacity of 50 MW. Supported by a thermal energy storage (TES) system capable of storing 1200 MWht energy in molten salt. Through a comprehensive assessment spanning 41 months, real-time operational data is analysed to evaluate the CSP-TES system's efficacy within Kuwait's climatic conditions. Various performance parameters are examined, providing insights into CSP technology's practical implications and operational strategies. Driven by the annual difference in downtime and TES availability, comparative analysis of the plant's annual electricity production over the three years reveals notable variations: 74.99 GWh in 2019, 160.62 GWh in 2020, and 140.04 GWh in 2021, corresponding to capacity factors of 19 %, 40.75 %, and 35.53 %, respectively. The findings underscore the strong correlation between the SF and TES system in determining overall plant efficiency. The study also highlights the impact of high wind speeds on plant operation, triggering shutdowns on 9 days during the assessment period. Additionally, soiling losses in the SF are assessed through daily reflectivity measurements, guiding cleaning efforts using semi-automated armed trucks. Seasonal peaks in cleanliness factor coincide with local spring season, underlining the detrimental effect of reflectivity losses on plant output due to higher dust mass densities. A design flaw causing frequent breakdowns of receiver tubes under high winds is identified and addressed.

Associations between daily food insecurity and parent and child well-being.

Food insecurity poses a serious threat to children's development, but the mechanisms through which food insecurity undermines child development are far less clear. Specifically, food insecurity may influence children through its effect on parents' psychological well-being and parent-child interactions as a result, but past research on the role of parents is correlational and undermined by omitted variable bias. Using a partially rural, low-income sample of parents living in Pennsylvania (N = 272, 90% mother, Mage = 35) and their school-aged children (ages 4-11, 50% female) alongside daily measures of parent-reported food insecurity and parent and child mood and behavior, we examine how daily changes in food insecurity predict daily changes in parent and child well-being, and the extent to which food insecurity operates through parents to affect children. This method not only explores families' daily, lived experiences of food insecurity, but improves upon the methodological issues undermining past research. Findings indicate that food insecurity influences parent and child well-being on a daily basis, but that associations are stronger and sustain longer for parents than children. Further, parent mood and behavior partially explain the association between daily food insecurity and child mood and behavior, but food insecurity is also independently associated with child well-being. This study is the first to examine daily associations between food insecurity and parent and child well-being. Its implications for food assistance programs, policies, and the future of food insecurity research are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Six-month pain and function outcome expectations were established for total knee arthroplasty using the smallest worthwhile effect.

Interpretations of patient-reported outcome measures following knee arthroplasty lack context and typically do not account for costs, risks and benefits compared to an alternative treatment. The primary purpose of our paper is to estimate expectations patients have for pain and function destination outcome, six-months following surgery relative to the outcome expected if knee arthroplasty was not done. Secondary purposes were to determine if statistically significant changes in the smallest worthwhile six-month outcome occurred following an interactive discussion and to assess the construct validity of the expected six-month outcome obtained at baseline. This was a secondary analysis of a prospective longitudinal cohort study of 121 patients undergoing knee arthroplasty. Smallest worthwhile effect estimates were determined and expected six-month KOOS Pain and Function, daily activity measures were established during a pre-operative visit. The average six-month expected (the destination of interest) KOOS Pain score was 75 (IQR = 64 to 86) and the average KOOS Function, daily activity score was 74 (IQR = 59 to 86). The smallest worthwhile effect discussion led to significant changes in expected destination scores. For example, KOOS Pain expected outcome changed from 87.7 (9.8) to 75.0 (13.6), a statistically significant reduction in expected outcome (t(119) = 16.942, p < 0.001. Six-month expected KOOS outcomes following knee arthroplasty were established and approximate the average six-month outcomes reported in the literature. Validity of these estimates was established. These data can be used to aid shared decision-making discussions regarding patient expectations of knee arthroplasty outcomes during a patient encounter.

Monitoring and analysis of pathogenic microorganisms and resistance genes.

The objectives are to improve the rapid identification method of microbial risk and cut off the route of transmission of resistance genes. When new pathogenic microorganisms are found, intervention can be carried out as early as possible to identify the risk of potential pathogen transmission, and timely cut off the transmission route. Hospital air samples were collected to analyse the distribution of environmental pathogenic microorganisms and the characteristics of ARGs resistance genes. The air samples were collected from 12 sampling sites in the Affiliated Hospital of Yangzhou University. In the infusion room, general ward and intensive care unit, no significant difference was found in microorganisms, and no significant difference was found in microbial resistance genes. There were some differences in resistance genes between east and west districts. Combined with the detection of pathogenic microorganisms and resistance genes in our hospital, it is necessary to improve the daily disinfection measures such as air conditioning and fresh air equipment, cut off the infection route, block the transmission of resistance genes, and monitor pathogens and resistance genes in airborne diseases.

Does ethanol-blended gasoline policy improve air quality in China?

Ethanol-blended gasoline has been increasingly adopted worldwide as a strategy to address pressing concerns related to energy security and air pollution. This study focuses on exploring how the pilot policy of promoting ethanol-blended gasoline for vehicles affects air quality. Using a panel dataset comprising daily air quality measurements spanning the period 2016–2020, we employ a staggered difference-in-difference (DID) approach to investigate the impact of this policy. The results reveal a significant improvement in air quality after the implementation of ethanol-blended gasoline policy. On average, the air quality index experiences a reduction of 9.71 units, accompanied by decreases in the concentrations of PM2.5, PM10, CO, and NO2 by 9.15 μg/m3, 10.28 μg/m3, 0.14 mg/m3, and 2.17 μg/m3, respectively. These findings remain consistent across a range of robustness checks. The heterogeneity analysis further indicates that cities with larger populations and more congested traffic observe a more pronounced improvement in air quality due to the implementation of ethanol-blended gasoline policy. Moreover, the cost-benefit analysis demonstrates that the estimated health benefits derived from the adoption of ethanol-blended gasoline, amounting to 12.73 billion yuan, outweigh the associated costs incurred by the policy implementation. The findings provide compelling evidence that supports the promising role of ethanol-blended gasoline policy in enhancing air quality and highlight the potential benefits of such a policy in addressing the challenges posed by air pollution.

Validation of TRMM and GPM Satellite Data Using Daily Precipitation Observations

Accurate precipitation data holds immense significance in hydrological analysis. A common challenge in this field often stems from the lack of comprehensive data availability. High-resolution satellite-based precipitation measurements covering large areas offer a potential solution. However, disparities in the resolution of observed rainfall data can impact data accuracy. The main goal of this study is to evaluate the accuracy of rainfall data obtained from the TRMM and GPM satellites in the Kuranji watershed. The evaluation was conducted on the performance of the GPM IMERG-F from the Integrated Multi-satellite Retrievals for the GPM mission and the TRMM 3B42RT on a daily scale spanning from 2015 to 2019 over the Kuranji watershed. The daily precipitation measurements were validated using three widely used statistical metrics (R, RMSE, and RB). The precipitation detection capability (POD, FAR, and CSI) was also considered in this assessment. The findings demonstrate that both satellite estimations exhibit a substantial correlation coefficient (0.68 for GPM, 0.62 for TRMM) with the measurements obtained from gauges, along with an inclination to overestimate precipitation. GPM IMERG-F and TRMM 3B42RT manifest a consistent spatial pattern in daily precipitation distribution, effectively representing the observed precipitation distribution. The greater probability of detection (POD), critical success index (CSI), and lower false alarm ratio (FAR) exhibited by GPM IMERG-F at varying rainfall intensities suggests its superior performance in accurately identifying observed precipitations. This finding supports the preference for GPM IMERG-F data over TRMM 3B42RT data across various applications, hydrology, and related disciplines in the future.

Examining within-person associations between alcohol and cannabis use and hooking up among adolescents and young adults in the United States

ObjectiveThis study examined daily associations between alcohol use, cannabis use, and simultaneous alcohol and cannabis/marijuana (SAM) use with the likelihood of hooking up (uncommitted sexual encounter that may or may not include intercourse). MethodWe used a longitudinal measurement burst ecological momentary assessment (EMA) design with 3-week EMA bursts with daily measurements repeated quarterly across 12 months. 1,009 (57 % female, Mean age = 20.00 [SD = 3.21]) Texan adolescents and young adults ages 15–25 participated in the study. Mixed effects logistic regression models were estimated using maximum likelihood estimation to evaluate the associations between substance use and hooking up. ResultsWithin-person results indicated that participants were more likely to hook up on days with alcohol use and on days with cannabis use, but not on days with SAM use. Participants were also more likely to hook up on drinking days with higher-than-usual alcohol use. Between-person results indicated that participants who used alcohol more often or cannabis more often on average were more likely to hook up, and participants who tended to drink more on drinking days were more likely to hook up. ConclusionsGiven the significant daily-level associations between alcohol and cannabis use and hooking up behavior, public health initiatives should focus on developing interventions to reduce alcohol and cannabis use and promote safer hooking up behavior among adolescents and young adults.

Combined 2D- and 3D ERT monitoring as a geophysical tool for investigating spatial and temporal soil moisture fluctuations in a pine-beech forest

As climate change continues, forests are increasingly suffering from drought stress, which is leading to widespread forest dieback, but also to increased mortality of individual trees. In this regard, the impact of small-scale differences in water availability on individual trees has not yet been sufficiently studied to determine possible responses of different tree species to future droughts. Since conventional soil moisture monitoring and sampling methods only consider single points or small volumes, Electrical Resistivity Tomography (ERT) is becoming increasingly important to cover a larger survey area and to detect small-scale heterogeneities with regard to soil properties and soil moisture.The current study describes the application of a combined two- and three-dimensional geoelectrical monitoring approach with daily measurements over two years (May 2021 – April 2023) in a forest ecosystem in Lower Franconia (northwestern Bavaria, Germany), which is strongly affected by climate change. Soil water content, soil matric potential, throughfall, and stem flow are also measured at the forest site as well as precipitation at an adjacent forest clearing.The seasonally (long-term) and precipitation-driven (short-term) temporal change of soil resistivity is correlated strongly with the measured soil water content and matric potential. The applied 3D-ERT approach also allowed a first three-dimensional monitoring of the subsurface below a European beech located in the middle of the measuring grid with a daily resolution. The corresponding results also provide first indications that besides soil moisture changes also chemical processes in the subsurface may influence temporal resistivity changes in the soil of forest sites.The results of this study show that daily 3D-ERT is very suitable for investigating small-scale as well as short- and long-term variations in soil moisture, which is becoming increasingly important for understanding the causal relationships considering tree mortality and the subsurface.

Design and methods of the mobile assessment of cognition, environment, and sleep (MACES) feasibility study in newly diagnosed breast cancer patients

Endocrine therapy (ET) for breast cancer treatment is associated with cognitive complaints, but their etiology is poorly understood. To address this, we developed and implemented an ambulatory assessment protocol consisting of wearable activity monitors, brief surveys of affect, context, and perceived impairments, and ultra-brief performance-based measures of cognition. Newly diagnosed, ER/PR+, stage 0-III, female breast cancer patients, were recruited. Ambulatory assessments were conducted on smart phones and wearable activity monitors were used to monitor sleep and physical activity. Participants were asked to complete five 7-day measurement bursts (one before starting ET and one each month for 4 consecutive months while on ET). We observed a consent rate of 36%, 27 women completed the study. Of the women that withdrew, 91% dropped prior to the midpoint of follow up. There were no significant differences in demographics, clinical breast cancer characteristics, sleep or physical activity patterns, or measures of cognition between women who completed versus withdrew. Women who did not complete the study provided fewer valid days of baseline data. In conclusion, while some women may be overwhelmed with their cancer diagnosis, we did not identify any predictive characteristics of women whom did not complete the study. This novel method enables the prospective study of psychological changes associated with cancer treatment, capturing a wide array of information about behavior, experience, and cognition, thus providing a picture of the lived experiences of cancer patients before and during exposure to ET.