Missing Rate Research Articles

The relationship between trust in federal oversight of vaccine safety and willingness to participate in COVID-19 clinical trials: a repeated measures study of Philadelphia residents (September 2021 – March 2023)

BackgroundThe Coronavirus Disease 2019 (COVID-19) pandemic precipitated an urgent need for clinical trials to discover safe and efficacious treatments. We examined how COVID-19 experiences, clinical trial awareness, and trust in the vaccine safety process were associated with willingness to participate in COVID-19 clinical trials. The objective was to investigate the relationship between trust in federal oversight of vaccine safety and willingness to participate in clinical trials for COVID-19 treatment across four distinct time points over an 18-month period during the COVID-19 pandemic.MethodsWe used four waves of data collected from September 2021 to March 2023 among 582 Philadelphia residents (with a missing data rate of 0.9%). Generalized estimating equations estimated the association between willingness to participate in COVID-19 clinical trials and participants’ trust in the federal government’s oversight of COVID-19 vaccine safety, COVID-19-related variables (COVID-19 related health challenges, history of COVID-19 infection), awareness of clinical trials and how to enroll in them, and sociodemographic characteristics (age, race/ethnicity, sexual orientation, gender, parental status, education, and insurance).ResultsOn average, willingness to participate in a COVID-19 clinical trial was positively associated with greater trust in the federal government’s oversight of vaccine safety [β = 0.34, 95% confidence interval (CI): 0.15–0.53], having COVID-19 (β = 0.40, 95% CI: 0.08–0.73), awareness of clinical trials (β = 0.38, 95% CI: 0.04–0.73), and knowledge of how to enroll (β = 0.83, 95% CI: 0.44–1.23). Among sociodemographic characteristics, race/ethnicity (p = 0.001) and gender (p = 0.018) were identified as predictors for COVID-19 trial willingness.ConclusionWillingness to participate in clinical trials may be bolstered by strengthening the public’s trust in the federal government’s role within vaccine safety oversight, increasing the perceived relevance of clinical trials to individuals’ health and well-being, and offering tailored information to educate diverse communities about ongoing trials and how to enroll in them.

Automated Versus Semi-Automated Lab Value Extraction for the VA Cardiac Surgical Quality Improvement Program

IntroductionThe Veterans Affairs Surgical Quality Improvement Program (VASQIP) trains surgical quality nurses (SQNs) at each Veterans Affairs (VA) hospital to extract or verify 187 variables from the medical record for all cardiac surgical cases. For ten preoperative laboratory values, VASQIP has a semiautomated (SA) system in which local lab values are automatically extracted, verified by SQNs, and lab values recorded at other VA facilities are manually extracted. The objective of this study was to develop and validate a method to automate the extraction of these ten preoperative laboratory values and compare results with the current SA method. Materials and methodsWe developed methods to extract ten preoperative laboratory values and measurement dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes. Automated (A) versus SA information extraction was compared in terms of agreement, conformance to data definitions, proximity to surgery, and missingness. ResultsFor surgeries with both A and SA lab values, the intraclass correlation coefficients for the ten variables ranged from 0.90 to 0.98. For several variables, the A method resulted in much lower rates of missing data (e.g., 2.4% versus 22.5% missing data for high-density lipoprotein) and eliminated out-of-date-range entries. ConclusionsAlthough SQN-extracted data are widely considered the gold standard within National Surgical Quality Improvement Programs, there may be advantages to fully automating extraction of lab values, including high congruence with SA SQN-extracted or verified values and lower rates of missingness and out-of-date-range data.

Remote assessment of the association between early executive function and mathematics skills

AbstractExecutive function (EF) skills are consistently associated with global mathematics assessments. However, less is known about which specific mathematics skills invoke EF in early childhood. We adapted batteries of EF, numerical, and patterning tasks to be conducted via synchronous video conferencing with typically developing 4‐year‐old children (N = 115, 56.6% female, 85.2% White, non‐Hispanic, 7.8% Multiracial, 0.9% Hispanic, 0.9% Black, 1.7% American Indian/Alaska Native, 2.6% Asian and 0.9% missing) from primarily upper middle class households (maternal education range: some college to graduate or professional degree, mode = graduate or professional degree) in the United States. We found feasibility of remote adaptation varied greatly by task. Just over half of enrolled children (57.02%) completed all tasks and there were higher rates of missingness compared to an in‐person sample with a similar age range collected in schools. Nonetheless, working memory predicted various mathematics skills, including patterning, verbal counting, and number comparison, controlling for age and maternal education. Relations between cognitive flexibility and patterning varied across model specifications. Overall, these findings provide insight into the costs and benefits of remotely administered direct assessments of EF and mathematics in early childhood and add to the growing body of literature suggesting working memory plays an important role in several foundational early mathematics skills. Suggestions are offered for remote assessment with preschoolers.Highlights Executive function and mathematics tasks were adapted for remote use and associations among skill sets were examined. Remotely administered data collection sessions had higher rates of missingness than comparable in‐person studies. Findings replicate and extend prior work on the associations between working memory and patterning, verbal counting, and number comparison in a remote setting.

Comparative analysis of the consistency of anesthesia depth between anesthesia index and Narcotrend index in monitoring pediatric tonsillectomy/adenoidectomy surgery

Objective: To analyze the consistency of the anesthesia index (AI) and Narcotrend index (NI) in monitoring the depth of anesthesia in pediatric tonsil/adenoidectomy. Methods: A total of 129 children who underwent elective tonsil/adenoidectomy at Xijing Hospital Affiliated to Air Force Medical University from July 2022 to October 2023 were prospectively enrolled. Both AI monitoring by ConView monitor and NI monitoring by Narcotrend monitor were conducted for children after they were admitted to the operating room, while Propofol-remifentanil combined with total intravenous anesthesia was used during the operation and the anesthetic dose was adjusted to maintain the NI value at 40-60. In addition, the AI and NI values were recorded at following time points: quiet eyes closing (T1), the beginning of induction (T2), loss of consciousness (T3), tracheal intubation (T4), the beginning of surgery (T5), during tonsil excision (T6), adenoidectomy (T7), the end of surgery (T8), consciousness recovery (T9) and tracheal extubation (T10). Bland-Altman consistency analysis was used to evaluate the consistency of the results of the two types of anesthesia depth monitoring, AI value and NI value. Receiver operator characteristic (ROC) curve and area under curve (AUC) were used to analyze the predictive efficacy of AI and NI values for the state of consciousness of children. The intraoperative awareness was followed up at 24 hours, 1 week and 1 month after surgery. Results: In 129 cases, there were 78 boys and 51 girls, with an average age of (7.2±3.2) years and an operation time of (72.8±14.0) min. No AI data were missing, but 13 NI data were missing, with a missing rate of 10.1% (13/129). At time points T1-T10, the AI values were 67.5±19.2 and the NI values were 67.2±19.2. Bland-Altman consistency analysis showed that the difference in value between AI and NI was 0.3 (95%CI:-0.1-0.6, P=0.120), with an out-of-consistency range of 4.0% (51/1 290). Before anesthesia induction, AI and NI values were maintained at a high level; with the beginning of anesthesia induction and the disappearance of the children's consciousness, both AI and NI values gradually decreased; after discontinuing the infusion of anesthetics, the values of AI and NI gradually increased, and the change trend of AI and NI in each stage of the perioperative period was consistent, and there was no significant difference between the two groups at each time point (all P>0.05). ROC curve analysis showed that the cut-off value for predicting the state of consciousness using AI was 72.5, with an AUC of 0.73 (95%CI: 0.70-0.75), and the cut-off value of NI was 79.5 and the AUC was 0.74 (95%CI: 0.72-0.77). There was no significant difference in AUC between the two indices (P=0.310). None of the children had intraoperative awareness. Conclusion: Both AI and NI can be used to monitor the depth of anesthesia in pediatric tonsil/adenoidectomy with high accuracy.

Behavioural interventions to treat anxiety in adults with autism and moderate to severe intellectual disabilities: The BEAMS-ID feasibility study.

The aim of this feasibility study was to adapt and model a behavioural intervention for anxiety with autistic adults with moderate to severe intellectual disabilities. Twenty-eight autistic adults with moderate or severe intellectual disabilities, 37 carers, and 40 therapists took part in this single-group non-randomised feasibility study designed to test intervention feasibility and acceptability, outcome measures, and research processes. The intervention was judged as feasible and acceptable by autistic adults with intellectual disabilities, carers, and therapists. Minor intervention revisions were suggested. Carers completed 100% of outcome measures and the missing data rate was low. Complying with legislation governing the inclusion of participants who lack capacity to decide whether they wanted to take part in this study led to an average 5-week enrolment delay. The intervention and associated study processes were judged to be feasible and acceptable and should now be tested within a larger randomised trial.

Benchmarking imputation methods for categorical biological data

Abstract Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, quantify species extinction risks, or evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance, due to incomplete sampling, limited data and resource availability. Several imputation methods exist to predict missing values and recent studies have explored their performance for continuous traits in biological datasets. However, less is known about the accuracy of these methods for categorical traits. Here we explore the performance of different imputation methods on categorical biological traits combining phylogenetic comparative methods, machine learning and deep learning models. To this end, we develop an open‐source R package, to impute trait data while integrating a simulation framework to evaluate their performance on synthetic datasets. We run a range of simulations under different missing rates, mechanisms, biases and evolutionary models. We propose an integration between phylogenetic comparative methods and machine learning imputation, and an ensemble approach, in which selected imputation methods are combined. Our simulations show that this approach provides the most robust and accurate predictions. We applied our imputation pipeline to an incomplete trait dataset of 1015 elasmobranch species (i.e. sharks, rays and skates) and found a high imputation accuracy of the predictions based on an expert‐based assessment of the missing traits. Overall, our R package facilitates the comparison of multiple imputation methods and allows robust predictions of missing trait values. Our study highlights the benefits of coupling phylogenetic evolutionary models with machine learning inference to augment incomplete biological datasets.

Assessing disparities through missing race and ethnicity data: results from a juvenile arthritis registry.

Ensuring high-quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieving the goal of inclusion of racial and ethnic minorities in scientific research and detecting disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network and assess impact of improved data completion on conclusions drawn from the registry. This is a mixed-methods quality improvement study that consisted of five parts, as follows: (1) Identifying baseline missing race and ethnicity data, (2) Surveying current collection and entry, (3) Completing data through audit and feedback cycles, (4) Assessing the impact on outcome measures, and (5) Conducting participant interviews and thematic analysis. Across six participating centers, 29% of the patients were missing data on race and 31% were missing data on ethnicity. Of patients missing data, most patients were missing both race and ethnicity. Rates of missingness varied by data entry method (electronic vs. manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared with patients with non-missing race and ethnicity data at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow-up compared with White patients. There was no significant change in odds ratio of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values, which may affect the ability to detect changes in odds ratio of cJADAS ≥5 after completion. About one-third of the patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data were not uniformly distributed compared with those with non-missing race and ethnicity data at baseline, suggesting that differences in outcomes after completing race and ethnicity data may be seen with larger sample sizes.

Object detection based deinterleaving of radar signals using deep learning for cognitive EW

In a real-world environment, multifunction radars (MFRs) pose major challenges to the electronic support system that is a part of cognitive electronic warfare. One of the main problems is deinterleaving of signals belonging to MFRs. The ability of MFRs to change their carrier frequency, pulse width, and pulse repetition interval (PRI) from pulse to pulse makes the deinterleaving task challenging. In this paper, an object detection based deinterleaving approach exploiting amplitude patterns caused by radar beam motions, which are determined based on radar antenna scan types, is proposed to deinterleave MFR signals. Amplitude patterns are created in two-dimensional images using amplitude (AMP) and time of arrival (TOA) parameters obtained from radar pulses. Deinterleaving is performed using a deep learning algorithm applied to these images. To the best of the authors’ knowledge, object detection based deinterleaving of radar signals using AMP-TOA images has not been considered so far. Contrary to the common approaches, in which searching for PRI patterns is required, amplitude patterns formed by the radar beam motions on the electronic support system are used in the proposed method. This enables robust identification of signals of MFRs having PRI, carrier frequency, and pulse width agility. With the proposed method, the intention of the radar, i.e., search or tracking, can be acquired in the deinterleaving stage, which ensures earlier situational awareness. The performance of the method is evaluated for varying number of radar signals from one to five through simulations, in which scenarios with missing pulses at different rates are considered. Simulation results demonstrate that, on the average, more than 0.98 mean average precision (mAP50) is achieved at 30% missing pulse rate. The performance of the method for search radar signals is slightly lower compared to that achieved for tracking radar signals due to the lower number of pulses received by the system. However, more than 0.93 AP50 is achieved for search radar signals in the presence of five radar signals with different missing pulse rates. Besides, real-time performance can be achieved using the proposed method on the GPU platform.

A new disease mapping method for improving data completeness of syndromic surveillance with high missing rates

AbstractSyndromic surveillance is a type of public health surveillance that utilizes nonspecific indicators or symptoms associated with a particular disease or condition to detect and track disease outbreaks early. However, data completeness has been a significant challenge for syndromic surveillance systems in many countries. Incomplete data may make it difficult to accurately identify anomalies or trends in surveillance data. In this study, a new disease mapping method based on a high‐accuracy, low‐rank tensor completion (HaLRTC) algorithm is proposed to estimate the quarterly positivity rate of the human influenza virus (IFV) based on highly insufficient 2010–2015 respiratory syndromic surveillance data from the subtropical monsoon region of China. The HaLRTC algorithm is a spatiotemporal interpolation method applied to fill in missing or incomplete data using a low‐rank tensor structure. The results show that the accuracy (R2 = 0.880, RMSE = 0.037) of the proposed method is much higher than that of three traditional disease mapping methods: Cokriging, hierarchical Bayesian, and sandwich estimation methods. This study provides a new disease mapping approach to improve the quality and completeness of data in syndrome surveillance or other familiar systems with a large proportion of missing data.

Design and Experiment of Ordinary Tea Profiling Harvesting Device Based on Light Detection and Ranging Perception

Due to the complex shape of the tea tree canopy and the large undulation of a tea garden terrain, the quality of fresh tea leaves harvested by existing tea harvesting machines is poor. This study proposed a tea canopy surface profiling method based on 2D LiDAR perception and investigated the extraction and fitting methods of canopy point clouds. Meanwhile, a tea profiling harvester prototype was developed and field tests were conducted. The tea profiling harvesting device adopted a scheme of sectional arrangement of multiple groups of profiling tea harvesting units, and each unit sensed the height information of its own bottom canopy area through 2D LiDAR. A cross-platform communication network was established, enabling point cloud fitting of tea plant surfaces and accurate estimation of cutter profiling height through the RANSAC algorithm. Additionally, a sensing control system with multiple execution units was developed using rapid control prototype technology. The results of field tests showed that the bud leaf integrity rate was 84.64%, the impurity rate was 5.94%, the missing collection rate was 0.30%, and the missing harvesting rate was 0.68%. Furthermore, 89.57% of the harvested tea could be processed into commercial tea, with 88.34% consisting of young tea shoots with one bud and three leaves or fewer. All of these results demonstrated that the proposed device effectively meets the technical standards for machine-harvested tea and the requirements of standard tea processing techniques. Moreover, compared to other commercial tea harvesters, the proposed tea profiling harvesting device demonstrated improved performance in harvesting fresh tea leaves.

A novel data-characteristic-driven modeling approach for imputing missing value in industrial statistics: A case study of China electricity statistics

As a direct reference tool to reflect the operational status and development level of national industry, industrial statistics hold significant value for numerous systematic studies. Nevertheless, it is crucial to recognize that the quality of these statistics can be compromised by the common occurrence of missing value. This issue poses a substantial challenge for analyzing and utilizing industrial statistics, impeding progress in tasks reliant upon them. Given the severity of the missing value problem in industrial statistical databases and the limitations of existing literatures on missing value imputation in terms of research objects and modeling approaches, this paper proposes a novel missing value imputation modeling approach for single-indicator panels of industrial statistics based on the idea of data-characteristic-driven (DCD). Accordingly, taking the inter-provincial “monthly power generation” data from China as an example, the imputation model was constructed and its validity was tested under different imputed objects (Jiangsu and Jilin), different missing types (continuous and discrete), and different missing rates (5%, 10% and 20%) respectively. The results indicate that the proposed DCD modeling approach in this paper exhibits excellent efficacy. The imputation model, constructed based on the data characteristic of the imputed object, demonstrates clear advantages in handling missing value with different missing types and rates. This is evident in its superior consideration of numerical accuracy, directional accuracy, and imputation stability, resulting in an outstanding comprehensive imputation effect.

Longitudinal economic burden of incident complications among metabolic syndrome populations

BackgroundThis study quantifies the longitudinal economic burden for a wide spectrum of incident complications, metabolic syndrome (MS)-related risk factors, and comorbidities in patients with MS.MethodsThis retrospective study utilized linked data from the 2013 National Health Interview Survey and the 2012–2021 National Health Insurance Research Database to identify MS individuals and their characteristics. The incidence rate of each complication was calculated as the number of complication events in the study period divided by the total person-years during follow-up. The healthcare costs of complications were analyzed using a generalized estimating equation model to determine the cost impact of complications after adjustment for patients’ characteristics. Sensitivity analyses on variables with high missing rates (i.e., cause of death, body mass index) were performed.ResultsAmong 837 identified MS individuals over 8.28 (± 1.35) years of follow-up, the most frequent complications were microvascular diseases (incidence rate for nephropathy/retinopathy/neuropathy: 6.49/2.64/2.08 events per 100 person-years), followed by cardiovascular diseases (2.47), peripheral vascular diseases (2.01), and cancers (1.53). Death was the costliest event (event-year cost per person: USD 16,429) and cancers were the most expensive complications (USD 9,127−11,083 for non-MS- and MS-related cancers). Developing non-MS/MS-related cancers, cardiovascular diseases, and obesity-related medical conditions increased annual costs by 273% (95% CI: 181−397%)/175% (105−269%), 159% (118−207%), and 140% (84−214%), respectively. Microvascular diseases had the lowest cost impact on annual costs (i.e., 27% [17−39%]/27% [11−46%]/24% [11−37%] increases for nephropathy/neuropathy/retinopathy, respectively). Having existing comorbidities increased annual costs by 20% (osteoarthritis) to 108% (depression). Having morbid obesity (i.e., body mass index ≥ 35 kg/m2) increased annual costs by 58% (30−91%).ConclusionsThe economic burden from costly incident complications (i.e., cardiovascular diseases, peripheral vascular diseases, cancers), MS-related risk factors (i.e., morbid obesity), and comorbidities (i.e., depression) highlight the urgent need for early intervention to prevent MS and its progression. The comprehensive cost estimates reported in this study can facilitate the parameterization of economic analyses to identify cost-effective interventions for these patients.

A road network traffic flow data imputation method based on the fusion of spatiotemporal features and adversarial networks

In response to the problem of missing traffic flow data on highways, to solve the problem of insufficient mining of traffic flow characteristics using existing spatiotemporal correlation repair methods, a missing data repair method based on spatiotemporal fusion adversarial network is proposed based on analyzing the spatiotemporal characteristics of traffic flow, this method utilizes the fusion of GRU and GCN to capture the fine-grained spatiotemporal relationships of traffic flow, optimizes the generator and discriminator of the adversarial network, and achieves accurate repair of missing data. Experiments based on real data have shown that under various missing modes and rates, the model can learn the topological relationships of the road network, capture the temporal regularity and spatiotemporal correlation in the data, and effectively impute missing data.

Efficient enhancement of low-rank tensor completion via thin QR decomposition.

Low-rank tensor completion (LRTC), which aims to complete missing entries from tensors with partially observed terms by utilizing the low-rank structure of tensors, has been widely used in various real-world issues. The core tensor nuclear norm minimization (CTNM) method based on Tucker decomposition is one of common LRTC methods. However, the CTNM methods based on Tucker decomposition often have a large computing cost due to the fact that the general factor matrix solving technique involves multiple singular value decompositions (SVDs) in each loop. To address this problem, this article enhances the method and proposes an effective CTNM method based on thin QR decomposition (CTNM-QR) with lower computing complexity. The proposed method extends the CTNM by introducing tensor versions of the auxiliary variables instead of matrices, while using the thin QR decomposition to solve the factor matrix rather than the SVD, which can save the computational complexity and improve the tensor completion accuracy. In addition, the CTNM-QR method's convergence and complexity are analyzed further. Numerous experiments in synthetic data, real color images, and brain MRI data at different missing rates demonstrate that the proposed method not only outperforms in terms of completion accuracy and visualization, but also conducts more efficiently than most state-of-the-art LRTC methods.

Tensor nonconvex unified prior for tensor recovery

Tensor data, such as hyperspectral images and multi-frame videos, have gained significant attention in practical applications. However, the inherent degradation phenomena during data acquisition, including noise and missing pixels, give rise to a series of ill-posed inverse problems that need to be addressed. Currently, the rational exploration of prior knowledge for tensor recovery, including global low-rankness and local smoothness, has emerged as a common concern. Inspired by recent notable works, this paper proposes a novel tensor non-convex unified prior term, which employs weighted tensor Schatten p-norm as a rank surrogate function in the gradient domain. The new prior can yield a regularizer that effectively captures low-rankness and smoothness, and is applied to tensor completion and tensor robust principal component analysis models. An efficient algorithm is developed by using the alternating direction method of multipliers and its convergence analysis is also provided. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods, particularly in cases of high missing rates and strong noise levels.

Electronic medical records imputation by temporal Generative Adversarial Network

The loss of electronic medical records has seriously affected the practical application of biomedical data. Therefore, it is a meaningful research effort to effectively fill these lost data. Currently, state-of-the-art methods focus on using Generative Adversarial Networks (GANs) to fill the missing values of electronic medical records, achieving breakthrough progress. However, when facing datasets with high missing rates, the imputation accuracy of these methods sharply deceases. This motivates us to explore the uncertainty of GANs and improve the GAN-based imputation methods. In this paper, the GRUD (Gate Recurrent Unit Decay) network and the UGAN (Uncertainty Generative Adversarial Network) are proposed and organically combined, called UGAN-GRUD. In UGAN-GRUD, it highlights using GAN to generate imputation values and then leveraging GRUD to compensate them. We have designed the UGAN and the GRUD network. The former is employed to learn the distribution pattern and uncertainty of data through the Generator and Discriminator, iteratively. The latter is exploited to compensate the former by leveraging the GRUD based on time decay factor, which can learn the specific temporal relations in electronic medical records. Through experimental research on publicly available biomedical datasets, the results show that UGAN-GRUD outperforms the current state-of-the-art methods, with average 13% RMSE (Root Mean Squared Error) and 24.5% MAPE (Mean Absolute Percentage Error) improvements.

Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey

This study aims to analyze the thunderstorm (TS) events in the megacity Istanbul by using thermodynamic indices and atmospheric stability parameters for the period of 2001–2022. It was determined that TS events did not show any trend on an annual basis, mostly (%69) occurred in the warm season (May–September), and mostly (%93) lasted for a few hours (0–3 h). The thermodynamic indices and atmospheric stability parameters used in the study are Showalter Index (SI), Lifted Index (LI), Severe Weather Threat Index (SWEAT), K-Index (KI), Totals Totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), and Bulk Richardson Number (BRN). Annual and seasonal analyzes of all indices and parameters were performed for TS and non-TS events. Significant differences were found in both average, maximum, and minimum values. The Probability of Detection (POD), False Alarm Ratio (FAR), Miss Rate (MR), Critical Success Index (CIS), Hiedke Skill Score (HSS), and True Skill Score (TSS) were used to analyze the success of the threshold values presented in the literature in detecting TS events. Then, the seasonal successes of these threshold values were tested. It was observed that the performance of the selected indices varied across seasons. The highest predictive skill was generally observed during the summer season, with the POD value ranging between 0.58 and 0.97 and the TSS value varying between 0.32 and 0.57. Conversely, the lowest predictive skill was typically observed during the winter season, where the POD value ranged from 0.00 to 0.75 and the TSS value varied between 0.00 and 0.40. The ideal threshold values were determined for indices and parameters by increasing or decreasing the existing threshold values at certain rates. Success increases of up to 15% in skill scores for the proposed threshold values.

High-resolution spatiotemporal inference of urban road traffic emissions using taxi GPS and multi-source urban features data: a case study in Chengdu, China

The spatial heterogeneity and temporal variability of traffic in urban environments make traffic emissions inference challenging. To address this challenge, this study introduces a novel geographical context-based approach utilizing high-resolution taxi GPS data, incorporating multidimensional contextual factors such as road data, points of interest (POI), weather data, and population density. The proposed method can enhance the precision of traffic emissions inference compared to conventional macroscopic estimation techniques. To overcome the issue of missing data in traffic emissions inference from taxi data, three ensemble machine learning algorithms—Random Forest, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost)—are employed. These algorithms efficiently handle a substantial volume of taxi GPS data, achieving reduced computational time and model complexity. The proposed framework establishes localized models for each road segment, taking into consideration both geographical and external features that characterize the urban environment. This localized modeling contributes significantly to a more profound understanding of traffic dynamics. A thorough comparative analysis is conducted to assess the performance of the proposed method. Results indicate that incorporating multidimensional urban features is advantageous for traffic speed inference. Among the ensemble learning models, Random Forest outperforms others when dealing with a small missing rate or limited sample size, while XGBoost exhibits superior performance for larger missing rates or substantial sample sizes. Additionally, an analysis of the feature importance in traffic speed highlights that road network features are the most significant factors, followed by temporal characteristics, spatial attributes, POI data, and weather information. Finally, leveraging inferred traffic speed and volume information, emissions from large-scale urban road traffic are inferred based on the COPERT model. In contrast to methods relying on complex, multi-source data for emission estimation, our approach utilizes simple and easily accessible data, enabling precise estimation of emissions on a large-scale spatiotemporal basis.

Optimal seeding rate enhances seedling quality, mechanical transplanting quality, and yield in hybrid rice.

To determine the appropriate seeding rate for machine-transplanted hybrid rice, field experiments were conducted during 2022-2023 using the hybrid rice variety Huazhe You 210 as the material. Four seeding rate treatments were set up: 40 (T1), 60 (T2), 80 (T3) and 100g tray-1 (T4), to investigate the effects of seeding rate on the seedling quality, transplanting quality, yield formation, and economic benefits of high-quality indica hybrid rice seedlings. The results showed that with increasing seeding rate, the seedling base stem diameter and seedling plumpness of hybrid rice seedlings decreased, but the root entwining force gradually increased, leading to a deterioration in individual seedling quality but an improvement in collective characteristics. As the seeding rate increased, the missing hill rate during mechanical planting of hybrid rice significantly decreased, while the number of seedlings per hill and the damaged seedling rate showed an upward trend. The growth volume of tillers, tillering spikelet rate, and harvest index of hybrid rice in the field showed an overall downward trend with increasing seeding rate, while the accumulation of dry matter initially increased and then decreased. The yield and economic benefits of hybrid rice grains showed an initial increase followed by a decrease with increasing seeding rate, with the highest yield and economic benefits achieved with the T2 treatment. In conclusion, the appropriate seeding rate for machine-transplanted hybrid rice is T2 (60g tray-1), which can maintain good seedling quality and improve transplanting quality, coordinate larger collective growth and appropriate harvest index, contributing to high yield and good economic benefits.

Alliance A222001: A randomized, double-blind, placebo controlled phase II study of oxybutynin versus placebo for the treatment of hot flashes in men receiving androgen deprivation therapy.

LBA12004 Background: Hot flashes are among the most common adverse events impacting quality of life reported by patients receiving androgen deprivation therapy (ADT) for the treatment of prostate cancer. Oxybutynin is an effective therapy for reducing frequency and severity of hot flashes in women. Pilot information supports that this drug may also benefit men with hot flashes related to ADT. Methods: Patients with prostate cancer receiving a stable regimen of ADT with at least 28 hot flashes per week were randomized to receive either oral oxybutynin 2.5 mg twice a day, oxybutynin 5 mg twice a day, or matching placebo doses for 6 weeks. The primary endpoint was the change in patient-reported hot flash scores (determined by multiplying the number of hot flashes by the mean hot flash severity [grade 0: none, 1: mild, 2: moderate, 3: severe, and 4: very severe]) from baseline to 6 weeks, as measured by a daily hot flash diary. A total of 87 patients provided 76% power to reject the null hypothesis of no between-arm mean difference in hot flash score reduction from baseline to 6 weeks, when comparing each oxybutynin arm to the combined placebo arms. This was based on a two-sided contrast estimated from a generalized linear mixed model, α= 0.10, intraclass correlation of 0.50, population standardized mean difference of 0.50, and 10% missing data rate. Results: 88 patients were accrued between 10/28/21 and 12/02/23. Six patients cancelled before starting treatment and one was ineligible, leaving 81 analyzed patients with a median age of 68. Baseline characteristics were balanced between arms with patients reporting an average of 10.15 (SD = 5.55) hot flashes per day and an average daily hot flash score of 18.23 (SD = 13.48) at enrollment. On average, patients on the placebo arm, low dose oxybutynin arm, and high dose oxybutynin arm had reductions of 2.15, 4.77, and 6.89 hot flashes/day, respectively. Compared to placebo arm patients, high dose oxybutynin arm patients had a greater reduction in hot flashes/day (p < 0.001), as did low dose oxybutynin arm patients (p = 0.02). Daily hot flash scores for the same three protocol arms reduced by an average of 4.85, 9.94, and 13.95 points, respectively. Compared to placebo arm patients, high dose oxybutynin arm patients had a greater reduction in daily hot flash scores (p = 0.002), as did low dose oxybutynin arm patients (p = 0.07). No treatment-related grade 3+ adverse events occurred. The most commonly reported oxybutynin-related grade 2 adverse event was dry mouth. Conclusions: Oxybutynin is superior to a placebo for the management of hot flashes in men associated with androgen deprivation therapy and appears to be well tolerated. Support: UG1CA189823; https://acknowledgments.alliancefound.org . Clinical trial information: NCT04600336 .