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The fast-paced advances in technology and scientific knowledge in the 21st century call for learners to possess professional skills that complement their technical skills to make meaningful contributions in communities. This article introduces a cross-disciplinary leadership training program, Graduate Scholars Leadership, Engagement, and Development (GS LEAD), that was designed and implemented to train students in professional skills including problem-solving, interdisciplinary teamwork, leadership, communication, and engagement. This training program provided learning opportunities for incoming graduate students to work beyond their laboratories, across disciplines, and into communities to identify real-world problems and design sustainable solutions. The design and implementation of the program, the findings of participating students' development in Program Year 1, and suggestions for future program design are discussed.

Introduction: This study examined the association between sugar-sweetened beverage consumption before the first 24 months of life and attention-deficit/hyperactivity disorder (ADHD). Methods: A population administrative cohort study was conducted in Korea (2008–2019) using linked national insurance data and a health screening survey. The cohort included 25,305 children in the exposed group with high sugar-sweetened beverage drinks (≥200 mL) and 339,931 in the reference groups (<200 mL) at 24 months of age. The primary outcome was the development of ADHD based on the International Classification of Disease (ICD) codes. Cox proportional model was used to identify the association between sugar-sweetened beverage consumption during early childhood and the later development of ADHD while controlling for multiple risk factors. Results: Over a mean follow-up period of 9.2 years, the incidence rates of ADHD were 29.6 and 23.8 per 10,000 person-years in the exposed and reference groups, respectively. Compared with the reference group, children consuming high-sugar drinks were at an increased risk of ADHD (adjusted hazard ratio 1.17, 95% confidence interval [CI] 1.08–1.27). These associations remained significant even after applying alternative ADHD definitions or adjusting for confounding variables. Conclusion: Children who consume sweetened beverages during early childhood are at increased risk of developing ADHD later in life. [ABSTRACT FROM AUTHOR]

Methylcyclohexane (MCH) dehydrogenation is an equilibrium‐limited reaction that requires high temperatures (>300 °C) for complete conversion. However, high‐temperature operation can degrade catalytic activity and produce unwanted side products. Thus, a hybrid zeolite membrane (Z) is prepared on the inner surface of a tubular support and used it as a wall in a membrane reactor (MR) configuration. Pt/C catalysts is packed diluted with quartz sand inside the Z‐coated tube and applied the MR for MCH dehydrogenation at low temperatures (190–250 °C). Z showed a remarkable H2‐permselectivity in the presence of both toluene and MCH, yielding separation factors over 350. The Z‐based MR achieved higher MCH conversion (75.3% ± 0.8% at 220 °C) than the conventional packed‐bed reactor (56.4% ± 0.3%) and the equilibrium state (53.2%), owing to the selective removal of H2 through Z. In summary, the hybrid zeolite MR enhances MCH dehydrogenation at low temperatures by overcoming thermodynamic limitations and improves the catalytic performance and product selectivity of the reaction. [ABSTRACT FROM AUTHOR]

An extensive series of wind-tunnel experiments were conducted in the Boundary Layer Wind Tunnel (BLWT) Experimental Facility (EF) at the University of Florida (UF) to investigate the effect of heterogenous terrain on wind flow and pressure distributions on building surfaces. Many studies have already been performed on the effect of upwind terrain on wind characteristics and wind loads on buildings. Previous tests in wind tunnels were mainly designed for uniform upwind terrain or simple two-dimensional roughness changes (e.g., smooth-to-rough change or vice versa). Using the wind-tunnel facility at the UF, we were able to perform an extensive series of tests on heterogeneous upwind terrains. The first part of the testing was focused on wind characteristics by measuring the three components of wind velocity when wind flow passed over different complex upwind terrains. The second part of the testing was intended to obtain the aerodynamic loads of wind over complex terrains on low-rise and midrise building models. The data and metadata are publicly available on the DesignSafe-CI repository under the DOI of 10.17603/Ds2-6hg9-R131. Researchers and practicing engineers can use the collected data to understand the effect of heterogeneous upwind terrain better. [ABSTRACT FROM AUTHOR]

BACKGROUND: The speech reception threshold (SRT), synonymous with the speech recognition threshold, denotes the minimum hearing level required for an individual to discern 50% of presented speech material. This threshold is measured independently in each ear with a repetitive up-down adjustment of stimulus level starting from the initial SRT value derived from pure tone thresholds (PTTs), measured via pure-tone audiometry (PTA). However, repetitive adjustments in the test contributes to increased fatigue for both patients and audiologists, compromising the reliability of the hearing tests. OBJECTIVE: Determining the first (initial) sound level closer to the finally determined SRT value, is important to reduce the number of repetitions. The existing method to determine the initial sound level is to average the PTTs called pure tone average (PTAv). METHODS: We propose a novel method using a machine learning approach to estimate a more optimal initial sound level for the SRT test. Specifically, a convolutional neural network with 1-dimensional filters (1D CNN) was implemented to predict a superior initial level than the conventional methods. RESULTS: Our approach produced a reduction of 37.92% in the difference between the initial stimulus level and the final SRT value. CONCLUSIONS: This outcome substantiates that our approach can reduce the repetitions for finding the final SRT, and, as the result, the hearing test time can be reduced. [ABSTRACT FROM AUTHOR]

Endothelial‐to‐mesenchymal transition (EndMT) is a complex biological process of cellular transdifferentiation by which endothelial cells (ECs) lose their characteristics and acquire mesenchymal properties, leading to cardiovascular remodeling and complications in the adult cardiovascular diseases environment. Melatonin is involved in numerous physiological and pathological processes, including aging, and has anti‐inflammatory and antioxidant activities. This molecule is an effective therapeutic candidate for preventing oxidative stress, regulating endothelial function, and maintaining the EndMT balance to provide cardiovascular protection. Although recent studies have documented improved cardiac function by melatonin, the mechanism of action of melatonin on EndMT remains unclear. The present study investigated the effects of melatonin on induced EndMT by transforming growth factor‐β2/interleukin‐1β in both in vivo and in vitro models. The results revealed that melatonin reduced the migratory ability and reactive oxygen species levels of the cells and ameliorated mitochondrial dysfunction in vitro. Our findings indicate that melatonin prevents endothelial dysfunction and inhibits EndMT by activating related pathways, including nuclear factor kappa B and Smad. We also demonstrated that this molecule plays a crucial role in restoring cardiac function by regulating the EndMT process in the ischemic myocardial condition, both in vessel organoids and myocardial infarction (MI) animal models. In conclusion, melatonin is a promising agent that attenuates EC dysfunction and ameliorates cardiac damage compromising the EndMT process after MI. [ABSTRACT FROM AUTHOR]

Purpose: This manuscript presents RADCURE, one of the most extensive head and neck cancer (HNC) imaging datasets accessible to the public. Initially collected for clinical radiation therapy (RT) treatment planning, this dataset has been retrospectively reconstructed for use in imaging research. Acquisition and Validation Methods: RADCURE encompasses data from 3346 patients, featuring computed tomography (CT) RT simulation images with corresponding target and organ‐at‐risk contours. These CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were manually generated and reviewed at weekly RT quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution's radiation treatment planning and oncology information systems using a custom‐built data mining and processing system. Furthermore, images were linked to our clinical anthology of outcomes data for each patient and includes demographic, clinical and treatment information based on the 7th edition TNM staging system (Tumor‐Node‐Metastasis Classification System of Malignant Tumors). The median patient age is 63, with the final dataset including 80% males. Half of the cohort is diagnosed with oropharyngeal cancer, while laryngeal, nasopharyngeal, and hypopharyngeal cancers account for 25%, 12%, and 5% of cases, respectively. The median duration of follow‐up is five years, with 60% of the cohort surviving until the last follow‐up point. Data Format and Usage Notes: The dataset provides images and contours in DICOM CT and RT‐STRUCT formats, respectively. We have standardized the nomenclature for individual contours—such as the gross primary tumor, gross nodal volumes, and 19 organs‐at‐risk—to enhance the RT‐STRUCT files' utility. Accompanying demographic, clinical, and treatment data are supplied in a comma‐separated values (CSV) file format. This comprehensive dataset is publicly accessible via The Cancer Imaging Archive. Potential Applications: RADCURE's amalgamation of imaging, clinical, demographic, and treatment data renders it an invaluable resource for a broad spectrum of radiomics image analysis research endeavors. Researchers can utilize this dataset to advance routine clinical procedures using machine learning or artificial intelligence, to identify new non‐invasive biomarkers, or to forge prognostic models. [ABSTRACT FROM AUTHOR]

The Senior Meaning in Life Evaluation scale encompasses not only older adults' personal motivation and growth but also the meaning for them in society and in their relationships: With this scale, we aimed to present their voices. A three-phase process was followed: The scale's items were developed empirically from interviews of older adults; exploratory factor analysis (EFA) was conducted to test convergent and concurrent validity; and finally, confirmatory factor analysis (CFA) was performed. EFA resulted in 18 items grouped into 4 factors (i.e., proactive on life, overcoming emptiness, acceptance in life, and social contribution), which was supported by the CFA. [ABSTRACT FROM AUTHOR]

Extranodal extension (ENE) is strongly associated with worse outcomes in patients with head and neck squamous cell carcinoma (HNSCC) and is included in staging systems for HNSCC2,3. While histopathological determination remains the gold standard, pathologic confirmation is not always available for every patient or may not be available at the time when evidence of ENE may alter the treatment plan. This study aims to develop an automated method of pre-treatment radiographic imaging ENE (iENE) detection without human intervention to increase the chances of clinical adoption and improve risk stratification of patients with ENE. In a previous study by Kann et al, a deep learning model was used to predict the presence of iENE in pre-treatment computed tomography (CT) scans of lymph nodes (LNs) cropped along their borders1. The model trained on 2673 LNs from 270 patients achieved a notable AUC score of 0.91 on the held-out test set, and 0.84 when tested on multi-institutional data. This performance gap may be attributed to the strong correlation between ENE status and LN diameter, as revealed by their regression analysis of clinical variables. Additionally, deploying their model in clinical settings necessitates substantial human involvement, as it requires contouring single or multiple suspicious LNs for inference. To address these issues, we introduce a deep learning model designed to predict patient-level ENE without LN segmentations. Not relying on manual segmentation or cropping along the LNs borders improves potential adoption and serves as a regularization technique to reduce feature correlation with LN volume. We utilized a retrospective dataset comprising 922 oropharyngeal cancer patients and their radiotherapy (RT) treatment planning CT scans, each annotated by a radiologist for the presence of iENE. Our model analyzes 256x256x128 voxels centered around the larynx, encompassing LNs across all neck levels. Due to the input's considerable size, conventional 3D convolutional neural networks proved inefficient due to GPU memory limitations. To circumvent this, we leveraged ACS convolutions (ACSConv), which adapt 2D convolutions for 3D volumetric images, allowing us to capitalize on ImageNet pre-trained weights for more robust feature learning and quicker convergence. Our dataset was divided into 759 cases for training and 163 for held-out testing, with the date of diagnosis as the separation point, simulating a pseudo-prospective in-silico trial. The model was trained across four cross-validation folds on the training set, and the results from these four models were ensembled during inference. Our ACSConv models, without pre-training, achieved an area under the receiver operating characteristic curve (AUROC) of 0.80-0.83 and an area under the precision-recall curve (AUPRC) of 0.47-0.61 on the held-out test set, whereas our ImageNet pre-trained ACSConv model improved with an AUROC of 0.87-0.91 and an AUPRC of 0.52-0.69. This highlights the benefit of using ImageNet pre-trained weights, which encapsulate robust features learned from millions of standard images. However, it's crucial to recognize that these performance metrics solely gauge the model's alignment with iENE, not histopathology. To demonstrate the model's risk stratification capabilities, we examined overall survival using Kaplan-Meier curves. The radiologist's iENE determination did not yield a statistically significant difference (p=0.01), whereas the model's predictions resulted in significant differences (p<0.005). The increased prognostic power may be attributed to inherent limitations of radiological determination compared to histopathology. Alternatively, the model may be less sensitive and highlight severe cases, increasing its prognostic power. External validation on unseen cohorts will further elucidate the model's prognostic capabilities and limitations. [Display omitted] Additionally, we assessed the model's predictions against the volume of the largest LN gross tumor volume (GTVn) and found a moderate Spearman's correlation coefficient of 0.531. This suggests that while the model does consider GTVn volume, it certainly incorporates information beyond LN borders, potentially shedding light on issues with GTVn determination. [Display omitted] To account for potential variability in larynx segmentation models, we explored the impact of test-time augmentations on model inference. This simulated variations in larynx segmentation and its effect on iENE model performance. Notably, we observed no significant differences in prediction standard deviations between patients with or without iENE. An interesting trend emerged, showing that prediction standard deviations increased as the model's confidence decreased (closer to 0.5). This underscores the necessity for a deeper investigation into deep learning model behaviors to enhance safety and increase the likelihood of clinical adoption. [Display omitted] Our study presents a novel deep learning model for the automated detection of pre-treatment iENE. With improved performance and prognostic capabilities, this model holds potential for enhancing risk stratification in HNSCC patients and merits further validation on external datasets and in clinical practice. [ABSTRACT FROM AUTHOR]

With the purported future of oncological care being precision medicine, the hunt for predictive biomarkers has become a focal point. A potential source lies in radiological imaging, which has motivated the field of radiomics for the last decade [1–4]. Radiomics research, however, has been hampered by inconsistent methodology, despite efforts to establish standard features [5]. The release of the open-source PyRadiomics toolkit [6] was a significant and necessary step to standardize radiomics analysis, but the collation and distribution of publically available radiomics datasets remains poorly organized within the community. As a result, significant overhead remains when dealing with multiple training, testing, and validation datasets from both internal and external sources. Further, a recent study has raised the question of whether radiomic features with high predictive value are surrogates for tumour volume measurements [7]. There is a need for standard methodology for radiomic feature extraction, as well as large, publicly available radiomic datasets that have undergone rigorous processing to benchmark analyses. In this study, we have developed a reproducible, automated, open-source processing pipeline to generate analysis-ready radiomics data. We showcase the pipeline's capabilities by processing and analyzing the largest publicly available head and neck cancer (HNC) dataset, RADCURE [8], and compare three previously published radiomics models [1,7,9] using the resulting data. Data outputs have been made available via https://www.orcestra.ca/, a web-app that hosts processed 'omics data. Our proposed pipeline leverages three main tools: Med-ImageTools [10], PyRadiomics [6], and ORCESTRA [11]. While the former two are imaging-specific, we have modified ORCESTRA to work with clinical radiological data. The proposed pipeline was developed using the RADCURE [12] dataset. It consists of 3,346 HNC CT image volumes, corresponding radiotherapy structure sets (RTSTRUCT) containing primary gross tumour volume (GTVp) contours, and clinical data. The Med-ImageTools library was used to generate complete file lists for each CT acquisition, associate these with the correct RTSTRUCT, and load both as Simple ITK [13] images. For each GTVp, preprocessing, quality checking, and radiomic feature extraction was performed using PyRadiomics. Extraction settings from the RADCURE prognostic modelling challenge [8] were applied. Feature extraction was repeated with two negative control samples for each CT, either by shuffling voxel index values or randomly generating voxel values within the range of values in the original CT [7] (Figure 1). [Display omitted] The standard for data organization on ORCESTRA [11] is the MultiAssayExperiment R object [14], designed to harmonize multiple experimental assays from an overlapping patient set. To leverage this for radiomics, each set of extracted features becomes an experiment, with clinical data included as the primary metadata describing each patient. To demonstrate the pipeline's utility, we replicated previously published survival analysis models with the training and test cohorts from the RADCURE challenge subset [8]. Coefficients from the MW2018 [7] and Kwan [9] models were used to calculate prognostic index values for the test cohort. For comparison, we fit a Cox model to the RADCURE training cohort using the same radiomic signature and applied it to the test cohort. A univariate model for GTVp Mesh Volume was also tested. All models were compared using the concordance index. We processed 2,949 patients with GTVp contours, for a total of 2,988 GTVps from patients with varying primary tumour sites. We extracted 1,317 radiomic features from the CT and the negative control volume for each GTVp. For the 37 patients with multiple GTVps, features were extracted independently for each contour. The final data object containing all of these features, along with the clinical data and PyRadiomics configuration file, are available at https://www.orcestra.ca/radiomicset/10.5281/zenodo.8332910. The pipeline implementation is published at https://github.com/BHKLAB-DataProcessing/RADCUREradiomics. Results from our radiomics analysis are available in Table 1. The subset of 2400 GTVs was split into training and test cohorts based on the 'RADCURE-challenge' label in the clinical data. The Kwan model was tested with the oropharynx patients only. Model performance is similar whether the features were extracted from the CT or negative control samples, signaling that the radiomic signature is likely highly correlated with tumour volume, a known confounder of radiomics analysis. This is confirmed by the comparable performance of the univariable volume model. [Display omitted] This standardized architecture framework and the publicly available processed RADCURE dataset can be used to benchmark new datasets or radiomics models semi-automatically. Future work will include organ at risk and nodal targets in the RADCURE dataset and the production of ORCESTRA objects for other publicly available HNC datasets. We anticipate that this pipeline and the RADCURE objects generated could be a standard testing benchmark for future radiomics analyses and publications. [ABSTRACT FROM AUTHOR]

This case study investigates two of the wind-induced vehicle accidents that occurred on a cable-stayed bridge in Korea over the past 20 years. Both of these incidents happened at the approach deck of the bridge, where the elevation from the sea level was only about 57% of the main span height. The investigation was focused on determining whether specific structural components or geometrical characteristics around the accident locations were responsible for these incidents. To achieve this, scaled-down vehicle and bridge deck models were used in a series of wind-tunnel tests. The results show that the wind load on the vehicles increased by 3–4 times near the accident locations. The primary cause of this anomaly was attributed to the rapid change in deck width. Furthermore, a risk assessment, which considered traffic and wind environments, showed that the risk level at the accident location was 100 times higher than at any other location on the bridge due to these unique characteristics. Additionally, further risk assessment revealed that two measures, traffic control and wind barrier installation, could mitigate the risk at the accident location. [ABSTRACT FROM AUTHOR]

Background: The relationship between epilepsy and depression is bidirectional. One condition exacerbates the other. However, there are no current guidelines for treating depression in epilepsy patients. In some cases, seizures worsen when antidepressants (AD) are prescribed or when they are discontinued due to adverse events. The Shugan Jieyu capsule, composed of Acanthopanax senticosus and Hypericum perforatum, is a widely used herbal medicine for treating depression. This study aimed to explore the effectiveness and safety of Shugan Jieyu capsules (SJC) in relieving depression in patients with epilepsy. Methods: We searched English, Korean, Japanese, and Chinese databases in October 2023 to collect all relevant randomized clinical trials (RCTs). The primary outcomes were the depression scale scores and seizure frequency. The secondary outcomes were quality of life (QoL) and adverse events. Results: Nine RCTs were included in this meta-analysis. Compared with AD, SJC showed significant differences in the improvement of depression (SMD: 3.82, 95% CI: 3.25, 4.39) and reduction in seizure frequency (MD: 0.39 times/month, 95% CI: 0.28, 0.50). SJC showed more beneficial results than antiepileptic drugs (AED) in terms of antidepressant effects (SMD: 1.10, 95% CI: 0.69, 1.51) and QoL (MD: 11.75, 95% CI: 10.55, 12.95). When patients were prescribed AED, the additional administration of SJC improved depression symptoms (SMD: 0.96, 95% CI: 0.28, 1.63). The SJC treatment group had a lower incidence of side effects than the control group. However, the difference was not statistically significant. Conclusions: Our results suggest that SJC may be effective in treating depression in patients with epilepsy. Additionally, SJC has the potential to help reduce seizure frequency in epilepsy patients with depression. [ABSTRACT FROM AUTHOR]

This study evaluates the efficacy and safety of fractional CO2 lasers for treating genitourinary syndrome of menopause (GSM) in Korean women. The patients received three laser applications at an interval of 4 weeks each. The severity of GSM symptoms was assessed using a visual analog scale (VAS) at baseline and at every visit. The objective scale was measured using the vaginal health index score (VHIS) and Vaginal Maturation Index (VMI) after completion of the laser procedure. During each procedure, the patients' pain in the VAS score was recorded. In the last visit, patients evaluated their satisfaction with the laser therapy using a 5-point Likert scale. Thirty women completed all the study protocols. After two sessions of laser therapy, some GSM symptoms (vaginal dryness and urgency) and VHIS improved significantly. After completion of the treatment, all GSM symptoms improved (p < 0.05), and the VHIS further increased significantly (VHIS at baseline, 8.86 ± 3.2 vs. V3, 16.83 ± 3.15, p < 0.001). The average satisfaction was 4.3. This study shows that fractional CO2 laser treatment is effective and safe for Korean women with GSM. Further studies are needed to confirm these results and assess the long-term effects of laser therapy. [ABSTRACT FROM AUTHOR]