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This paper shares the reflections of a small group of graduate students and faculty members in the United Arab Emirates (UAE) on the challenges and affordances of using large language model (LLM) tools to assist with academic writing in an English Medium Education (EME) context. The influence of interpretive grounded theory afforded the authors the opportunity to engage with emerging data from a focus group interview. Ethical issues including academic integrity and maturity formed a major theme of this study, as well as the future-thinking affordances of LLMs in facilitating and democratizing academic writing for all, including those in EME programs. Considering that LLMs are here to stay and will be used by students and faculty alike, the authors consider that the nature of assessment is likely to change and indeed will require higher education institutions to consider the types of assessments in place, with a view to potentially modifying them in light of these technological advances. We recommend the use of deeply personalized, critically reflective writing assignments where students demonstrate how the topic has meaning in their individual context and personal life story, that will ensure academic integrity and maturity while still embracing these new technologies to widen the scope of academic writing.

ABSTRACT: The advent of immunological therapies has revolutionized the treatment of solid and haematological cancers over the last decade. Licensed therapies which activate the immune system to target cancer cells can be broadly divided into two classes. The first class are antibodies that inhibit immune checkpoint signalling, known as immune checkpoint inhibitors (ICIs). The second class are cell‐based immune therapies including chimeric antigen receptor T lymphocyte (CAR‐T) cell therapies, natural killer (NK) cell therapies, and tumour infiltrating lymphocyte (TIL) therapies. The clinical efficacy of all these treatments generally outweighs the risks, but there is a high rate of immune‐related adverse events (irAEs), which are often unpredictable in timing with clinical sequalae ranging from mild (e.g. rash) to severe or even fatal (e.g. myocarditis, cytokine release syndrome) and reversible to permanent (e.g. endocrinopathies).The mechanisms underpinning irAE pathology vary across different irAE complications and syndromes, reflecting the broad clinical phenotypes observed and the variability of different individual immune responses, and are poorly understood overall. Immune‐related cardiovascular toxicities have emerged, and our understanding has evolved from focussing initially on rare but fatal ICI‐related myocarditis with cardiogenic shock to more common complications including less severe ICI‐related myocarditis, pericarditis, arrhythmias, including conduction system disease and heart block, non‐inflammatory heart failure, takotsubo syndrome and coronary artery disease. In this scientific statement on the cardiovascular toxicities of immune therapies for cancer, we summarize the pathophysiology, epidemiology, diagnosis, and management of ICI, CAR‐T, NK, and TIL therapies. We also highlight gaps in the literature and where future research should focus.

Abstract In this research work, we synthesized a Schiff base derivative, N,N-dimethyl-4-{[(4-nitrophenyl)imino]-methyl}aniline, denoted as (n1). The molecule (n1) was characterized using spectroscopic analyses, including FT-IR, NMR 1H, and 13C. Our compound (n1) is an unsaturated molecule, consisting of two benzylic rings connected by a methylimine bridge. The resulting system comprises seven alternating π bonds. At both ends of (n1) and in the para position, there are the N(CH3)2 group with a strong electron-donating effect and the NO2 group with a strong electron-accepting effect. The molecular structure of our compound prompted us to evaluate and study its properties in the field of NLO. The assessment of NLO properties is conducted by determining the Egap and employing density functional theory (DFT) quantum chemistry studies. The optical gap of (n1), measured using the Tauc method, is found to be 2.7 eV, serving as a reference value for the choice of the DFT functional in theoretical calculations. Quantum chemistry studies were carried out using Gaussian09 software, and the results were visualized with GaussView05. CAM-B3lyp functional was chosen for theoretical calculations due to its close agreement with experimental values. The studies confirm that (n1) exhibits significant NLO properties. Additionally, NBO (Natural Bond Orbital) analyses provide insight into the mechanism and trajectory of intramolecular charge transfer in (n1).

Since asphalt pavement bleeding and raveling are significant threats to road safety, comfort, and service life, timely and accurate detection of them is desirable and cost-effective to develop pavement maintenance programs. Moreover, due to the fact that both of these distresses (i.e., raveling and bleeding) affect the texture of pavements, simultaneous classification of these distresses can provoke challenges. Thus, this study explores efficient texture analysis of 2D images for bleeding and raveling detection using tree-based ensemble methods. To this end, two scenarios of feature extraction are taken into account using Histogram Equalization (HE), Local Binary Pattern (LBP), and Gray-Level Co-occurrence Matrix (GLCM) techniques. Based on these two feature sets, four tree-based ensemble methods were evaluated for classifying the three classes of Bleeding, Raveling, and No-Bleeding & No-Raveling. The results confirmed that Feature Set B which derived from the integration of HE-GLCM and LBP-GLCM feature extraction provided the most favorable outcome, and gained the high performance of F1-Score ≅ 97 %. Finally, Convolutional Neural Network (CNN) models were constructed using the collected dataset and compared to the proposed method.

Abstract Determining the genetic contributions to Parkinson’s disease (PD) across diverse ancestries is a high priority as this work can guide therapeutic development in a global setting. The genetics of PD spans the etiological risk spectrum, from rare, highly deleterious variants linked to monogenic forms with Mendelian patterns of inheritance, to common variation involved in sporadic disease. A major limitation in PD genomics research is lack of racial and ethnic diversity. Enrollment disparities have detrimental consequences on the generalizability of results and exacerbate existing inequities in care. The Black and African American Connections to Parkinson’s Disease (BLAAC PD) study is part of the Global Parkinson’s Genetics Program, supported by the Aligning Science Across Parkinson’s initiative. The goal of the study is to investigate the genetic architecture underlying PD risk and progression in the Black and/or African American populations. This cross-sectional multicenter study in the United States has a recruitment target of up to 2,000 individuals with PD and up to 2,000 controls, all of Black and/or African American ancestry. The study design incorporates several strategies to reduce barriers to research participation. The multifaceted recruitment strategy aims to involve individuals with and without PD in various settings, emphasizing community outreach and engagement. The BLAAC PD study is an important first step toward informing understanding of the genetics of PD in a more diverse population.

Background: One of the main reasons for neonatal deaths is preterm delivery, and infants who have survived preterm birth (PB) are at risk of significant health complications. However, an effective method for reliable and accurate prediction of preterm labor has yet to be proposed.Objective: This study proposes an artificial neural network (ANN)-based approach for early prediction of PB, and consequently can hint physicians to start the treatment earlier, reducing the chance of morbidity and mortality in the infant.Material and Methods: This historical cohort study proposes a feed-forward ANN with 7 hidden neurons to predict PB. Thirteen risk factors of PB were collected from 300 pregnant women (150 with preterm delivery and 150 normal) as the ANN inputs from 2018 to 2019. From each group, 70%, 15%, and 15% of the subjects were randomly selected for training, validation, and testing of the model, respectively.Results: The ANN achieved an accuracy of 79.03% for the classification of the subjects into two classes normal and PB. Moreover, a sensitivity of 73.45% and specificity of 84.62% were obtained. The advantage of this approach is that the risk factors used for prediction did not require any lab test and were collected in a questionnaire. Conclusion: The efficacy of the proposed approach for the early identification of pregnant women, who are at high risk of preterm delivery, leads to necessary care and clinical interventions, applied during the pregnancy.

Abstract Through experimental tests, this paper investigated the effectiveness of microwave healing on asphalt mixtures containing iron powder (IP) filler to improve durability and enhance mechanical properties. The moisture sensitivity of the asphalt mixes with varying iron powder filler contents was measured using the modified Lottman test. For evaluating the asphalt mixture healing ability, two complementary methods were used: the fatigue-based method, derived from the ability of microwave healing of asphalt mixture samples damaged up to 50% level of indirect tensile fatigue (ITF), and the fracture-based method, obtained from the strength recovery rate of broken semi-circular samples by applying consecutive breaking-healing cycles. The results showed that the greater the quantity of iron powder filler in the asphalt sample, the greater the sample’s indirect tensile strength and TSR ratio, indicating that IP had a positive effect on moisture sensitivity. The findings also indicated that utilizing iron powder as a filler positively strengthens the fatigue life, increases the toughness, and increases the microwave healing indices (both fatigue and failure approaches). Finally, according to the study results, substituting 70% iron powder as the filler is the most suitable option for improving the mechanical and self-healing characteristics of asphalt mixes.

Abstract Aims The use of loop diuretics in pulmonary arterial hypertension (PAH) is less frequent compared with heart failure. The clinical and prognostic characteristics of PAH patients according to loop diuretic use remain unexplored. In this study, we retrospectively analysed the characteristics and survival of PAH patients requiring different doses of loop diuretics. Methods and results Patients diagnosed with PAH between 2001 and 2022 at seven European centres for the management of PAH. According to the median equivalent dose of furosemide in the overall cohort, patients were divided into two subgroups: no/low‐dose loop diuretic and high‐dose loop diuretic. Primary outcome was 5 year all‐cause mortality. Among the 397 patients included, 227 (57%) were treated with loop diuretics. Median daily furosemide equivalent dose was 25 mg, and accordingly patients were divided in no/low dose (i.e. ≤25 mg, n = 257, 65%) vs. high dose (i.e. >25 mg, n = 140, 35%). Patients in the high‐dose group were older, more likely to have comorbidities, and had a more severe disease according to the ESC/ERS risk category. Crude 5 year survival was significantly shorter in patients in the high‐dose group, but after adjustment for age, sex, and risk category, high loop diuretic dose was not significantly associated with the primary outcome. Conclusions Use of high dose of loop diuretics in PAH is associated with a higher burden of comorbidities, more severe disease, and worse survival. However, in PAH, the need of high loop diuretic dose is a marker of disease severity and not an independent prognostic factor.

Solar energy is one of the renewable and clean energy sources. Accurate solar radiation (SR) estimates are therefore needed in solar energy applications. Firstly, two deep learning models, including gated recurrent unit (GRU) and long short-term memory (LSTM), were developed in this study. Next, a data pre-processing technique named multivariate variational mode decomposition (MVMD) was used to construct the MVMD-GRU and MVMD-LSTM hybrid models. To better test the performance of proposed simple and hybrid models, four stations located in the Illinois State of the USA (i.e., Dixon Springs, Fairfield, Rend Lake, and Carbondale) were considered as the study sites. Whole the simple and hybrid models were established under two different strategies, i.e., local and external. In the local strategy, SR of each location was estimated using the minimum and maximum air temperatures from the same station. While, minimum and maximum air temperatures as well as SR data from the nearby station were utilized in external strategy to estimate SR time series of any target site. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics were used when evaluating the models performances. The overall results revealed that the proposed MVMD-GRU and MVMD-LSTM hybrid models illustrated better SR estimates compared to the simple GRU and LSTM in both the local and external strategies. The values of error metrics obtained for the superior hybrid models (i.e., MVMD-LSTM) during the testing period were as: RMSE = 2.532 MJ/m2.day, MAE = 1.921 MJ/m2.day, R2 = 0.916 at Dixon Springs; RMSE = 2.476 MJ/m2.day, MAE = 1.878 MJ/m2.day, R2 = 0.921 at Fairfield; RMSE = 2.359 MJ/m2.day, MAE = 1.780 MJ/m2.day, R2 = 0.924 at Rend Lake; RMSE = 2.576 MJ/m2.day, MAE = 1.941 MJ/m2.day, R2 = 0.914 at Carbondale. Therefore, the coupled models proposed in this study can be possibly recommended as suitable alternatives to the simple deep learning models with a reliable precision in estimating SR time series.

Future space exploration missions will expose astronauts to various stressors, making the early detection of mental stress crucial for prolonged missions. Our study proposes using functional near infrared spectroscopy (fNIRS) combined with multiple machine learning models to assess the level of mental stress. Objective: The objective is to identify and quantify stress levels during 240 days confinement scenario. In this study, we utilize a diverse set of stress indicators including salivary alpha amylase (sAA) levels, reaction time (RT) to stimuli, accuracy of target detection, and power spectral density (PSD), in conjunction with functional connectivity networks (FCN). We estimate the PSD using Fast Fourier Transform (FFT) and the FCN using partial directed coherence. Results: Our findings reveal several intriguing insights. The sAA levels increased from the first 30 days in confinement to the culmination of the lengthy 240-day mission, suggesting a cumulative impact of stress. Conversely, RT and the accuracy of target detection exhibit significant fluctuations over the course of the mission. The power spectral density shows a significant increase with time-in-mission across all participants in most of the frontal area. The FCN shows a significant decrease in most of the right frontal areas. Five different machine learning classifiers are employed to differentiate between two levels of stress resulting in impressive classification accuracy rates: 96.44% with-nearest neighbor (KNN), 95.52% with linear discriminant analysis (LDA), 88.71% with Naïve Bayes (NB), 87.41 with decision trees (DT) and 96.48% with Support Vector Machine (SVM). In conclusion, this study demonstrates the effectiveness of combining functional near infrared spectroscopy (fNIRS) with multiple machine learning models to accurately assess and quantify mental stress levels during prolonged space missions, providing a promising approach for early stress detection in astronauts.