bluuebunny/crossref_metadata_2025_split huggingface dataset:
Saharan dust in Central Europe: Impact on particulate matter characteristics in an urban and a natural locality
Authors: Loskot Jan , Jezbera Daniel , Nalezinkova Martina , Smejkalova Adela Holubova , Halboth Danilo , Komarek Jan 
Published: September 2024

Abstract

At the turn of March and April 2024, most of mainland Europe was afflicted by sand dust particles carried by wind flow from the Sahara Desert. As a result, Central Europe experienced an exceptionally high increase in air pollution. In this work, the impact of this Saharan dust event on PM10 characteristics in an urban and a natural locality in the Czech Republic was investigated. PM10 concentrations before and during the Saharan dust event were measured using the Beta-Attenuation method. During the Saharan dust event, they were about 6–8 times higher than under normal atmospheric conditions, exceeding WHO guidelines by up to 2 times. A potential effect of terrain and altitude on the local concentrations of Saharan dust was observed. Airborne dust collected before and during the Saharan dust event was then studied using scanning electron microscopy combined with energy-dispersive spectroscopy (single-particle analysis of sizes and elemental composition) and X-ray diffractometry (mineralogical composition). Surprisingly, the particle size distribution was not significantly affected by the Saharan dust advection, but its dependency on the sampling locality was revealed. The dominant mineral in the Saharan dust was calcite, which substantially altered the local PM10 composition. The studied Saharan dust probably originated from a natural area, as the amount of anthropogenic pollutants detected was negligible. Notably, its carbon content was lower compared with the usual local PM10. The elevated PM10 concentrations appear to be the most relevant risk associated with this Saharan dust event in Central Europe. The transported dust originated from the northern/north-western Sahara – probably from the Atlas region – which was verified by a backward trajectory analysis of air masses.
Consequential Validity Evidence of Yes-No Angoff Standard Setting in a Pre-Clinical Medical School Curriculum
Authors: Dimanche Ketsia , Klatt Edward , Angle Marshall 
Published: September 2024

Abstract

Background
Standard setting is consequential to student outcomes for defining sufficient mastery of knowledge and thus accurately classifying medical students as competent (or not) for advancing through a pre-clinical curriculum. In particular, for multiple-choice medical knowledge exams the Yes-No Angoff method for standard setting yields consequential cut-off scores based on faculty-experts’ item-level judgments of students possessing borderline but sufficient competence required for demonstrating sufficient mastery. Adapting a construct validity framework and defining consequential academic success in terms of unimpeded progress, we investigated consequential validity evidence of passing standards derived from the Yes-No Angoff method.
Methods
We analyzed academic success for four pre-clinical semesters across three student cohorts. First, we identified passing standards for pre-clinical courses using the Yes-No Angoff method. Then, we applied binary logistic regression and receiver-operator characteristic (ROC) analyses with area under the curve (AUC) to evaluate passing standards. For binary outcomes, we defined academic success in terms of unimpeded progress through the curriculum and students’ first-attempt passage of the United States Medical Licensing Examination (USMLE) Step 1. Model predictors included Yes-No Angoff passing standards, Medical College Admissions test scores, and grade point averages for math and science courses.
Results
ROC analyses showed a low but acceptable area under the curve for a single semester in one cohort and excellent or outstanding AUCs for the remaining 11 semesters. With rates ranging between 89% and 96%, overall classification accuracy for predicting academic success was adequate for all pre-clinical semesters across all cohorts.
Conclusions
Affirming that Yes-No Angoff standards accurately predicted academic success, study results indicated consequential validity evidence for our school’s passing standards in a pre-clinical medical curriculum.
Loss of Kat2b impairs intraflagellar transport and the Hedgehog signaling pathway in primary cilia
Authors: Cha Hwayeon , Jun Jae Hee , Ko Je Yeong , Kim Ho-Shik , Yoo Kyung Hyun , Park Jong Hoon 
Published: September 2024

Abstract

Primary cilia are sensory organelles that regulate various signaling pathways. For this, microtubules work as a highway, and they are tuned with post-translational modifications, such as acetylation. However, the role of acetylation in the regulation of primary cilia remains uncertain. Here, we identified K (lysine) acetyltransferase 2B (Kat2b) as a novel regulator of primary cilia. Kat2b localizes to the cytosol, centrosome, and the cilium basal body. Also, the basal expression of Kat2b gradually increases during ciliogenesis. Kat2b regulates the rate of cilia assembly and the acetylation of α-tubulin via its catalytic activity. The loss of Kat2b reduces the recruitment of intraflagellar transport (IFT) components to the ciliary axoneme and impairs Hedgehog signaling activation. In addition, Kat2b-knockout mice show mild abnormalities and ciliary IFT defect in kidneys. Thus, our new findings establish a link between acetylation regulated by Kat2b and its relevance to ciliary assembly and function.
Decoding Synthetic News: An Interpretable Multimodal Framework for the Classificationof News Articles in a Novel News Corpus
Authors: Schlee Michael , Kant Gillian , Ehrling Christoph , Säfken Benjamin , Kneib Thomas 
Published: September 2024

Abstract

Recent advancements in Artificial Intelligence (AI), notably the development of Large Language Models (LLMs)and text-to-image diffusion models, have facilitated the creation of realistic textual content and images. Specifically, platformslike ChatGPT and Midjourney have simplified creation of high-quality text and visuals with minimal expertise and cost. Theincreasing sophistication of Generative AI presents challenges in ensuring the integrity of news, media, and information quality,making it increasingly difficult to distinguish between real and artificially generated textual and visual content. Our workaddresses this problem in two ways. First, by means of ChatGPT and Midjourney we create a comprehensive novel multimodalnews corpus named SyN24News based on the N24News corpus, on which we evaluate our model. Second, we develop a novelexplainable synthetic news detector for discriminating between real and synthetic news articles. We leverage a Neural AdditiveModel (NAM) like network structure that ensures effect separation by handling input data in separate subnetworks. Complexstructures and patterns are extracted by deep features from unstructured data, i.e. images and texts, using fine-tuned VGGand DistilBERT subnetworks. We ensure further explainability by individually processing carefully chosen handcrafted textand image features in simple Multilayer Perceptrons (MLPs), allowing for graphical interpretation of corresponding structuredeffects. Our findings indicate that textual information are the main drivers in the decision finding process. Structured textualeffects, particularly Flesch-Kincaid reading ease and sentiment, have a much higher influence on the classification outcomethan visual features such as dissimilarity and homogeneity.
Single-cell RNA sequencing of murine limbal epithelia reveals Gas1 as a novel stem/progenitor cell marker for the corneal epithelium
Authors: Richardson Alexander , Corley Susan , Delic Naomi , Li Hue , Lloyd Andrew , Girolamo Nick 
Published: September 2024

Abstract

The corneal epithelium is endowed with a rare population of stem cells that reside within the limbus, a circumferential transition zone that partitions the cornea from the conjunctiva, thus referred to as limbal epithelial stem cells (LESC). Despite the surge in investigations using single-cell RNA sequencing (scRNA-seq) of the ocular surface, a unifying marker(s) that distinguish these cells from their progeny is yet to be identified. We used a keratin (K)-14-driven lineage-tracing system and SmartSeq-2 single-cell transcriptomics in 5-60-week-old mice to interrogate the identity of limbal epithelia. Four cell clusters were identified, derived from both Confetti+ and Confetti− cells (clusters 0–3), with cluster 3 designated to harbor progenitor cells. We found one gene of interest in cluster 3, growth arrest-specific gene 1 (Gas1) coding for a cell-surface protein. PCR, flow cytometry and immunofluorescence disclosed this gene to be rarely expressed in limbal epithelial cells. Gas1 was also co-expressed with K14 in both young and old mice and upregulated following a mild mechanical debridement injury to the central cornea. The cell-surface expression of this antigen can be used to identify, extract and enrich progenitor cells for downstream molecular investigations and for generating better-quality cell-based grafts to treat severe corneal disease.
Neural Representation and Semantic Segmentation for Enhanced Underwater Imaging
Authors: Song Jinhao , Wang Chenli , Song Xiaoqiang 
Published: September 2024

Abstract

Underwater images are often plagued by low brightness, contrast, and blurred vision due to sediment and light scattering, hindering effective feature extraction. This paper proposes SGNeR, a novel underwater image enhancement model leveraging semantic segmentation and implicit neural representation. SGNeR combines real and augmented scenes to retain more image details, utilizing semantic segmentation as supervision to improve image quality and segmentation accuracy. To reduce dependence on paired training data, a triple-closed-loop constraint module enables self-supervised learning. Thorough experiments demonstrate that SGNeR outperforms state-of-the-art methods, achieving superior visual and quantitative results on various benchmarks. Our approach offers a robust, data-driven solution for underwater image enhancement, with promising applications in marine exploration and research. The dataset we used and the example code are publicly available at this link : https://github.com/ssssxw/SGNeR
Dual Inhibition of AChE and MAO-B in Alzheimer's Disease: Machine Learning Approaches and Model Interpretations
Authors: Hou Qinghe , Li Yan 
Published: September 2024

Abstract

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases. Given the multifactorial pathophysiology of AD, monotargeted agents can only alleviate symptoms but not cure AD. Acetylcholinesterase (AChE) and Monoamine oxidase B (MAO-B) are two key targets in the treatment of AD, molecules that inhibiting both targets are considered promising avenue to develop more effective AD therapies. In the present work, a dual inhibition dataset containing 449 molecules was established, based on which six machine-learning algorithms (KNN, SVM, RF, GBDT, LGBM and DNN) four fingerprints (MACCS, ECFP4, RDKitFP, PubChemFP) and DRAGON descriptors were combined to develop 30 classification models in which GBDT model paired with MACCS and especially ECFP4, achieved the best performance across multiple metrics (Accuracy = 0.89, Precision = 0.88, Recall = 0.92, F1 Score = 0.90, MCC = 0.70). Moreover, based on the curated bioactivity datasets of AChE and MAO-B, regression models were developed to predict pIC50 values. For the AChE inhibition task, GBDT and SVM demonstrated the best performance (MSE = 0.43, MAE = 0.47, R2 = 0.75). The SVM algorithm emerged as the most effective for MAO-B inhibition (MSE = 0.35, MAE = 0.45, R2 = 0.77). The SHAP algorithm was used to interpret the optimal models, identifying and analyzing the key substructures and properties for both dual-target and single-target inhibitors. The interactions of these structural fragments with both targets were further validated using molecular docking.
Effective Supervision in Doctoral Studies through Constructive and Motivational Feedback: The CoMFeS Model
Authors: Singh Manvender Kaur Sarjit 
Published: September 2024

Abstract

This study investigates the critical role of effective supervision in the successful completion of doctoral studies, with a focus on constructive and motivational feedback. By examining both personal and professional dynamics between students and supervisors, the research emphasizes the importance of sustainable relationships throughout the doctoral journey. Drawing from qualitative data through semi-structured interviews and observations, this study highlights the significance of timely, specific, and supportive feedback in fostering student motivation, enhancing research skills, and improving academic performance. The findings suggest that effective feedback leads to higher student satisfaction, better research outcomes, and stronger supervisor-student relationships. Practical recommendations for supervisory training and feedback delivery are offered, aiming to enhance supervisory practices and inform institutional policies in doctoral education globally, particularly in the Malaysian context.
Fetal and Neonatal Outcomes in Booked vs. UnBooked Deliveries: A Systematic Review and Meta-analysis of the Global Literature
Authors: Mansoor Javeria , Hafeez Uffaira , Khan Noor Ullah , Ali Ahtisham , Zubair Rooja , Swathi N. L. , Nadeem Abdullah , Mansoor Faareah 
Published: September 2024

Abstract

Purpose
A booked case is an indicator of antenatal care (ANC), embodying a proactive approach to pregnancy care, whereas unbooked deliveries describe scenarios in which pregnant women delay seeking ANC, which may cause adverse fetal outcomes and lead to neonatal morbidity and mortality. Despite extensive exploration of the effects of ANC on maternal and fetal health, a detailed review comparing fetal outcomes in booked versus unbooked cases was lacking. Our study aimed to quantify the effects of ANC on fetal and neonatal outcomes through a comprehensive meta-analysis of booked and unbooked cases.
Methodology
This review was registered in PROSPERO (Registration No: CRD42024526568). Two databases were systematically searched, PubMed and Google Scholar, with a time filter of 2003–2023. Quality was assessed on the basis of the Newcastle‒Ottawa scale for cohort studies and the modified version for cross-sectional studies. The main outcomes were neonatal mortality, low Apgar scores, low birth weight (LBW), macrosomia, meconium aspiration and birth asphyxia. The secondary outcomes were neurological symptoms, neonatal intensive care unit (NICU) admissions, congenital anomalies, jaundice and neonatal sepsis. A random-effects model was used to evaluate the pooled effect size of the odds ratio (OR).
Results
In total, 28 studies were included in the final review, with the majority (n = 24) originating from low to middle-income countries. For the majority of the outcomes, being booked was found to be protective on the basis of pooled ORs, i.e., neonatal mortality (OR 0.30; 95% CI 0.15–0.63, I² = 96%), low APGAR scores at 1 minute (OR 0.33; 95% CI 0.29–0.38, I2 = 92% and 5 minutes (OR 0.16; 95% CI 0.08–0.32, I2 = 91%), LBW (OR 0.36; 95% CI 0.30–0.43, I2 = 46%), jaundice (OR 0.48; 95% CI 0.19–1.25, I2 = 74%), MAS (OR 0.36; 95% CI 0.18–0.70, I2 = 64%), neurological symptoms (OR 0.34; 95% CI 0.13–0.85, I²=20%), neonatal sepsis (OR 0.56; 95% CI 0.37–0.86, I2 = 17%), asphyxia (OR 0.39; 95% CI: 0.32–0.49; I2 = 0%) and however, we did not find any difference between booked and unbooked deliveries in terms of macrosomia (OR 0.99; 95% CI: 0.53–1.83, I² = 32%) and congenital anomalies (OR 0.09; 95% CI 0.35–2.29 I2 = 83%). The publication bias was minimal.
Conclusion
This meta-analysis provides substantial evidence that booked deliveries have significantly better fetal outcomes across a broad spectrum than unbooked deliveries do; therefore, interventions that improve ANC coverage need to be implemented to reduce adverse fetal outcomes.
Optimizing Pretraining Datasets for Large Language Models Through Recursive Perplexity Correlations
Authors: Audrad Samuel , Sullivan James , Bennett Olivia , Rossi Xavier 
Published: September 2024

Abstract

The exponential expansion of textual data has catalyzed the development of sophisticated language models, enabling unprecedented levels of language comprehension and generation. Introducing perplexity correlations as a mechanism for optimizing pretraining datasets constitutes a groundbreaking and substantial advancement, significantly enhancing the quality and effectiveness of the data employed in language model training. Through the careful analysis of perplexity scores in relation to various dataset attributes, this novel approach facilitates the identification and refinement of high-quality data segments, thereby constructing a more coherent and representative training corpus. The application of this methodology to the Mistral Large Language Model resulted in notable reductions in perplexity scores and marked improvements in performance across standard benchmarking evaluations. These enhancements demonstrate the critical importance of data quality in the training of language models and demonstrate that strategic dataset optimization via perplexity correlations can substantially elevate model performance and reliability. Consequently, this study provides a robust framework for ongoing advancements in language model development, emphasizing the significant role of data refinement in achieving superior natural language processing capabilities.
Inclusion of water age in conjunctive optimal operation of water and power networks
Authors: Shmaya Tomer , Ostfeld Avi 
Published: September 2024

ABSTRACT
Water distribution systems (WDSs) are vital infrastructures designed to deliver water safely to consumers. This complex system necessitates continuous operational decisions, often optimized for efficiency. WDSs rely on power grids (PGs) to operate pumps and treatment facilities. PGs, likewise crucial, require strategic management to meet demand and environmental standards. Integrating the operation of both systems has garnered attention for its potential cost, energy, and environmental benefits. By leveraging the interconnection, trade-offs can be assessed, leading to improved solutions. Past research primarily focused on cost or carbon emissions as well as on hydraulic and voltage constraints. However, this study examines the impact of power systems on water quality, particularly water age, a key operational concern. Incorporating PGs into the optimal operation problem may alter flow directions, thereby influencing water age. Through mathematical modelling, this study evaluates these effects and applies them to simple case studies, demonstrating the influence of PG operation on water quality. Results demonstrate how tank constraints affect water age and show that a conjunctive operation approach, although beneficial for reduction of cost and energy consumption, can be damaging for water quality.
Nanomedicine in Management of Cerebral Infarction and Brain Cancer: Role of Inflammation
Authors: Kumar Vikas , Al-Abbasi Fahad , Anwar Firoz , Lal Jonathan A. 
Published: September 2024

Introduction:
Cerebral infarction, the blockage of blood vessels in the brain,
is generally an age-related illness. Factors such as unhealthy diets, stressful behaviours
and decreased environmental consistency with physiological barriers also contribute to
increased casualties. Long-term brain function reconstruction and successful drug therapy
are needed. The most frequent malignant brain tumour, glioblastoma, has been linked
to variations in mitochondrial ROS, chaperone-mediated autophagy, and the interaction
between lncRNA (BC200) and miRNA. Glioblastoma stem cells express high levels of
ATP/P2X7 receptors, promoting survival by activating M2 muscarinic receptors.

Areas Covered:
This expert opinion provides an overview of the latest experimental
drug therapies aimed at protecting against and restoring cerebral stroke.

Expert Opinion:
Nanomedicine overcomes the challenges associated with traditional
therapy and physiological obstacles in the treatment of cerebral infarction by improving
stroke management, including diagnosis, imaging, and treatment, addressing a diverse
range of associated factors.
Trends in the Treatment of Chronic Wounds
Authors: Sharma Rohit , Hruska Jan , Peter Lukas , Randlova Kristina , Kuca Kamil 
Published: September 2024

Abstract:
Chronic wounds remain one of the significant burdens to health across the
world, mainly in view of diabetes and its natural consequences. This category of lesions
includes pressure ulcers, vascular diseases, and surgery-related wounds, which affect
millions and pose a major challenge to the healthcare industry. The paper reviews the
various physiological mechanisms of wound healing, factors that impede it, and some
new treatments emerging at this moment. In contrast, current developments include surgical
and non-surgical alternatives like topical dressings, medicated formulations, and
skin substitutes. Advanced wound care today covers tissue-engineered skin substitutes,
3D-printed wound dressings, topical medicated formulations, and growth factor-based
therapies. These are non-invasive, biocompatible methods that are cost-effective, userfriendly,
and more conducive to natural healing than traditional therapies. Hydrogel
dressings have high water content to create a moist environment that encourages healing.
They also reflect excellent physicochemical and biological properties, which enhance
autolytic debridement and reduction of pain due to the moisture retention, biocompatibility,
and non-toxicity conferred. Tissue-engineered skin substitutes, comprising allogeneic
or autologous cells, wound-healing enhancement bioengineered allogeneic cellular
therapies are like the natural skin and encourage regeneration. 3D printing allows
the production of customized dressings to aid in better treatment. Newer therapies, including
bioengineered allogeneic cellular therapies and fish skin grafting, require more
clinical trials to confirm safety and efficacy. With such innovations in wound healing
technologies and therapies, the future looks quite promising in managing chronic
wounds, enhancing healing, reducing healthcare expenditure, and promoting a better
quality of life for patients.