bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Optimization Path of Higher Education Management Based on Collaborative Filtering Algorithm
Authors: Keke Shi
Published: October 2024

In order to better realize the optimization of university education management, this paper puts forward the research on the optimization path of university education management under collaborative filtering algorithm. The optimization of higher education management is divided into several management directions. First, the teaching resource recommendation management, using collaborative filtering algorithm based on emotional tendency, analyzes the comments of users of teaching resources, and recommends the teaching resources to users next time according to the emotional tendency obtained from the analysis. Second, the library book recommendation management, constructing the characteristic model of library book borrowing users, uses collaborative filtering algorithm to generate nearest neighbors, calculating the similarity of users, and recommending books that meet their characteristics for users according to the similarity. Finally, the management of personalized course selection recommendation in colleges and universities, which uses the historical information of students to establish the evaluation matrix of course selection, searches the nearest neighbors according to the evaluation matrix, and produces the personalized course selection recommendation results of the students according to the nearest neighbors. According to the experiment, the recommended users can be satisfied with the recommended results in different directions of education management. After applying this method, the overall quality of college students has been significantly improved, the expenditure of education management in colleges and universities has been significantly reduced, and the optimization effect of education management in colleges and universities has been remarkable.
Design of an Educational Psychology Research Material Sharing Platform Based on Fuzzy Clustering Algorithm
Authors: Guangli Luo
Published: October 2024

In the interdisciplinary field of educational psychology, efficiently managing and accessing research materials remains a formidable challenge due to the vast and varied nature of available data. This paper introduces an innovative platform engineered to streamline the organization and dissemination of educational psychology research materials by harnessing the power of fuzzy clustering algorithms for automated content categorization. Motivated by the imperative to enhance the accessibility and discoverability of scholarly resources, our methodology employs the Fuzzy C-Means (FCM) algorithm, adept at accommodating the intrinsic ambiguity and overlap prevalent in educational psychology topics. The process initiates with a comprehensive preprocessing of diverse research materials, including scholarly articles, reports, and datasets, succeeded by meticulous feature extraction to distill pivotal themes and concepts. Subsequent application of the FCM algorithm facilitates the intuitive grouping of materials into clusters, each with varying degrees of relevance, thereby mirroring the multidimensional essence of the domain. Rigorous experimental validation of our approach underscores its efficacy in augmenting search precision and markedly diminishing the time scholars expend in locating pertinent materials. The platform’s user-centric interface further simplifies the exploration of educational psychology research, offering scholars a streamlined mechanism to access and engage with an extensive repository of resources. Through this study, we demonstrate a significant stride towards overcoming the hurdles in research material categorization and retrieval, heralding a new era of efficiency and accessibility for the educational psychology academic community on a global scale.
A Method for Predicting the Result of University Employment Education Transformation Based on Multi-Source Information Technology
Authors: Xu Shuqin
Published: October 2024

In the data on the outcomes of higher education employment transformation, there is high correlation among multiple independent variables, leading to multicollinearity. If multicollinearity exists, it can complicate the establishment of predictive analysis models and the interpretation of relationships between variables for higher education employment transformation outcomes. The employment issues of college graduates have become a focal point of social concern. These issues not only reflect the employment status of college graduates but also indirectly indicate the quality of higher education and the level of talent cultivation. Therefore, a prediction method for the effectiveness of higher education employment transformation based on multi-source information technology is proposed. According to the theory of multi-source information technology, an employment data classification framework is constructed to integrate multi-source information. The matching operator function output values are calculated, weights are determined, and the final college student information collection results are output. The standard error between employment unit types and salary ranges is analyzed, clustering training samples are trained, and the distance from each employment information sample to the centroid is calculated to obtain preprocessing results of the transformation outcomes, thus avoiding the impact of multicollinearity on the results of higher education employment transformation. A model structure for analyzing factors influencing higher education employment is established, allowing for an understanding of the characteristics of different types of educational resources and enabling the prediction of the effectiveness of higher education employment transformation. Experimental results show that this method can accurately monitor the dynamic employment situation of university students. After applying this method, the standard error and probability error values are significantly reduced, proving to be significantly effective in predicting the outcomes of higher education employment transformation.
Evaluation Method of Teaching Effect of Environmental Art and Design in Universities Based on Multi-Source Data Fusion Technology
Authors: Liu Guohua
Published: October 2024

The evaluation of teaching reform satisfaction is crucial for improving teaching quality. In the evaluation of satisfaction with teaching reforms in environmental art and design, issues such as the low reliability of evaluation indicator data can lead to unsatisfactory results. To address this, this paper proposes a new method for evaluating satisfaction with teaching reforms in environmental art and design based on multi-source data integration. By establishing principles for constructing a multi-source indicator system for evaluating satisfaction with teaching reforms in environmental art and design, a four-level multi-source indicator system was determined. This system mainly includes one first-level indicator, four second-level satisfaction evaluation indicators, and several third- and fourth-level evaluation indicators. The construction of the multi-source indicator system for evaluating satisfaction with teaching reforms in environmental art and design was completed. Using a four-part graph model and entropy weight calculation, the importance of the evaluation indicators for satisfaction with teaching reforms in environmental art and design was classified. The expert scoring method was introduced to correct the multi-source indicator data for evaluating satisfaction with teaching reforms in environmental art and design. Based on standardized indicator data, a penalty rule was added to the loss function of the multiple linear regression model to remove data noise, achieving pre-processing of the multi-source indicators for evaluating satisfaction with teaching reforms in environmental art and design. After completing the above process, first collect multi-source data from questionnaire surveys, student feedback, etc., and preprocess and map it to a unified ontology class. Subsequently, data features are extracted through non-negative matrix factorization, and the similarity between object types is calculated. Finally, based on the importance, redundancy, and interaction of data sources, an integrated quality model is designed to effectively integrate multiple sources of data, construct a satisfaction evaluation model, and output the score of satisfaction evaluation. The experimental results show that the proposed method has the highest satisfaction index, reaching about 0.99, indicating a high degree of recognition for the reform; when the amount of integrated data changes, the proposed method maintains the best integratison reliability. Even with an increase in data volume, its reliability only slightly decreases and stabilizes at a high level; meanwhile, the average absolute error of the proposed method is the lowest, consistently below 2.5%, indicating a high level of stability and accuracy in its evaluation results. The above results confirm that the proposed method can effectively evaluate the satisfaction of environmental art design teaching reform and improve teaching quality.
Research on the Construction Method of Curriculum Teaching Knowledge Graph Based on Bi-LSTM and CNN Algorithm
Authors: Liu Hui
Published: October 2024

The aim of the paper is to explore a method of constructing a curriculum teaching knowledge graph by combining Bi-LSTM and convolutional neural network (CNN) algorithm. The field of education is constantly seeking innovation to improve teaching results and student learning experience. Knowledge graph, as an advanced technology of structured representation of knowledge, is expected to provide effective support for teaching management and personalized learning. First, this paper introduces the background and significance of the curriculum teaching knowledge graph. By establishing knowledge maps, we can more clearly present the knowledge system and correlation in the curriculum, which helps teachers to design more targeted teaching content and provide personalized learning paths for students. However, traditional knowledge graph construction methods are often faced with problems such as incomplete information capture and inaccurate semantic association, so it is necessary to introduce advanced deep learning algorithms to improve the quality of knowledge graph. Secondly, this paper elaborates on the construction method of fusion Bi-LSTM and CNN algorithm. Bi-LSTM, as a recurrent neural network capable of capturing sequence information, can better model the evolution process of knowledge in the course. As a CNN is good at extracting local features, CNN can effectively capture the spatial structure information in the knowledge graph. By integrating two, we can improve the expression ability and reasoning accuracy of knowledge graph. Further, the experimental results show that the fusion Bi-LSTM and the CNN algorithm have significantly improved the accuracy of information capture and inference compared with the traditional method. In summary, this paper proposes an innovative construction method of curriculum teaching knowledge graph by integrating Bi-LSTM and CNN algorithm, which provides new ideas and solutions for informatization and personalized teaching in the field of education. In the future, the applicability of this method in different disciplines and teaching scenarios can be further discussed, and more advanced technologies can be combined to continuously improve and expand the research.
Synchronization of Generalized Lorenz Systems in a Loop
Authors: Rehák Branislav , Čelikovský Sergej , Lynnyk Volodymyr , Lynnyk Anna
Published: October 2024

The conditions for the synchronization of three interconnected generalized Lorenz systems are given. The interconnection topology contains a loop. It is shown that, under certain conditions on the strength of the coupling of the systems, the full synchronization of all three systems is guaranteed. The results are illustrated by an example.
Temporal-Enhanced Radar and Camera Fusion for Object Detection
Authors: Kong Linhua , Wang Yiming , Chang Dongxia , Zhao Yao
Published: October 2024

Recently, object detection methods based on multi-modal fusion have gained widespread adoption in autonomous driving, proving to be valuable for detecting objects in dynamic environments. Among them, millimeter wave (mmWave) radar is commonly utilized as an effective complement to cameras, as it is almost unaffected by harsh weather conditions. However, current approaches that fuse mmWave radar and camera often overlook the correlation between the two modalities, failing to fully exploit their complementary features. To address this, we propose a temporal-enhanced radar and camera fusion network to explore the correlation between these two modalities and learn a comprehensive representation for object detection. In our model, a temporal fusion model is introduced to fuse mmWave radar features from different moments, thus mitigating the problem of mmWave radar point-object mismatch due to object movement. Moreover, a new correlation-based fusion strategy using the dedicated mask cross-attention is proposed to fuse mmWave radar and vision features more effectively. Finally, we design a gate feature pyramid network that selects shallow texture information based on deep semantic information to obtain more representative features. The experimental results on the nuScenes benchmark demonstrate the effectiveness of our proposed method.
The importance on the use of active methods when teaching human morphophysiology
Authors: Rodrigues Rosângela Correa , Grossmann Nárjara Veras , Corrêa Rodrigues Mosar , Abreu Tainá de , Alexandre Aversi-Ferreira Tales , Lage de Sá Canabarro Soraya , Tavares Maria Clotilde Henriques
Published: October 2024

Traditional didactic methods have not been successful in promoting independent and reflective students. Thus new teaching techniques are essential to optimize the way students learn. This study aimed to evaluate the perception of students, from the Faculty of Ceilandia, University of Brasília (FCE/UnB), about the active method “inverted class,” which has as its primary characteristic to stimulate the student to study and research the contents before the theoretical classes or explanation of the teacher. To this end, 64 students of the summer course participated in the Flipped Classroom and later evaluated the discipline method. Initially, they received a script with clinical questions about the class’s content, which should be discussed and answered about the basic, clinical, and practical application concepts. To evaluate the didactic method applied, on the last day of class, the summer class students were invited to answer a survey about satisfaction when learning the contents. For 98% of the students, the teaching method used was approved. It is also emphasized that this class had 48 repeating students, unmotivated with the course because, for many, they declared themselves unable to be approved. Given the success of the method, we decided to apply it throughout 2018, so the method was applied to over 180 students of FCE/UnB health courses. In short, it is considered that the results presented here to support other studies that highlight the need for changes in traditional teaching methods, which is a didactic option in undergraduate courses.
Regulation of versican expression in macrophages is mediated by canonical type I interferon signaling via ISGF3
Authors: Chang Mary Y. , Chan Christina K. , Brune Jourdan E. , Manicone Anne M. , Bomsztyk Karol , Frevert Charles W. , Altemeier William A.
Published: October 2024

We report the novel finding that versican expression is regulated by the interferon-stimulated gene factor 3 (ISGF3) arm of canonical type I Ifn signaling in LPS-stimulated macrophages. This pathway is distinct from mechanisms that control versican expression in other cell types. This suggests that macrophage-derived versican may play a role in limiting a potentially excessive inflammatory response. The detailed understanding of how versican expression is regulated in different cells could lead to unique approaches for enhancing its anti-inflammatory properties.
Profiling growth performance, insulin-like growth factors, and IGF-binding proteins in rainbow trout lacking IGFBP-2b
Authors: Cleveland Beth M. , Izutsu Ayaka , Ushizawa Yuika , Radler Lisa , Shimizu Munetaka
Published: October 2024

Knocking out IGFBP-2b in rainbow trout improved food intake, growth performance, and feed conversion ratio and reduced serum IGF-1 by 0.5- to 0.8-fold, without changes in the concentration of free serum IGF-1. Based on these findings, we propose that, in addition to IGFBP-2b, the 32-kDa IGFBP (putative IGFBP-2a) also serves as a major carrier of circulating IGF-1 in salmonids.
Transition from acute kidney injury to chronic kidney disease: mechanisms, models, and biomarkers
Authors: Zhang Tingfang , Widdop Robert E. , Ricardo Sharon D.
Published: October 2024

Acute kidney injury (AKI) and chronic kidney disease (CKD) are increasingly recognized as interconnected conditions with overlapping pathophysiological mechanisms. This review examines the transition from AKI to CKD, focusing on the molecular mechanisms, animal models, and biomarkers essential for understanding and managing this progression. AKI often progresses to CKD due to maladaptive repair processes, persistent inflammation, and fibrosis, with both conditions sharing common pathways involving cell death, inflammation, and extracellular matrix (ECM) deposition. Current animal models, including ischemia-reperfusion injury (IRI) and nephrotoxic damage, help elucidate these mechanisms but have limitations in replicating the complexity of human disease. Emerging biomarkers such as kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), and soluble tumor necrosis factor receptors (TNFRs) show promise in early detection and monitoring of disease progression. This review highlights the need for improved animal models and biomarker validation to better mimic human disease and enhance clinical translation. Advancing our understanding of the AKI-to-CKD transition through targeted therapies and refined research approaches holds the potential to significantly improve patient outcomes.
Application of Protein Expression in Mycoplasma Study
Authors: Xie Nian
Published: October 2024

Mycoplasma is a kind of pathogenic microorganism, and its survival and replication need to be parasitic inside the host cell. Therefore, studies on the metabolic pathway, protein composition, and biological characteristics of Mycoplasma require the use of protein expression techniques. In this paper, the application of protein expression in Mycoplasma research was reviewed, including commonly used protein expression systems, optimization strategy of protein expression, protein omics analysis, and protein function research, and the future development direction has been prospected.
Support Vector Machine Outperforms Other Machine Learning Models in Early Diagnosis of Dengue Using Routine Clinical Data
Authors: Qaiser Ariba , Manzoor Sobia , Hashmi Asraf Hussain , Javed Hasnain , Zafar Anam , Ashraf Javed
Published: October 2024

Background: There is a dire need for the establishment of active dengue surveillance to continuously detect cases, circulating serotypes, and determine the disease burden of dengue fever (DF) in the country and region. Predicting dengue PCR results using machine learning (ML) models represents a significant advancement in pre‐emptive healthcare measures. This study outlines the comprehensive process of data preprocessing, model selection, and the underlying mechanisms of each algorithm employed to accurately predict dengue PCR outcomes.

Methods: We analyzed data from 300 suspected dengue patients in Islamabad and Rawalpindi, Pakistan, from August to October 2023. NS1 antigen ELISA, IgM and IgG antibody tests, and serotype‐specific real‐time polymerase chain reaction (RT‐PCR) were used to detect the dengue virus (DENV). Representative PCR‐positive samples were sequenced by Sanger sequencing to confirm the circulation of various dengue serotypes. Demographic information, serological test results, and hematological parameters were used as inputs to the ML models, with the dengue PCR result serving as the output to be predicted. The models used were logistic regression, XGBoost, LightGBM, random forest, support vector machine (SVM), and CatBoost.

Results: Of the 300 patients, 184 (61.33%) were PCR positive. Among the total positive cases detected by PCR, 9 (4.89%), 171 (92.93%), and 4 (2.17%) were infected with serotypes 1, 2, and 3, respectively. A total of 147 (79.89%) males and 37 (20.11%) females were infected, with a mean age of 33 ± 16 years. In addition, the mean platelet and leukocyte counts and the hematocrit percentages were 75,447%, 4189.02%, and 46.05%, respectively. The SVM was the best‐performing ML model for predicting RT‐PCR results, with 71.4% accuracy, 97.4% recall, and 71.6% precision. Hyperparameter tuning improved the recall to 100%.

Conclusion: Our study documents three circulating serotypes in the capital territory of Pakistan and highlights that the SVM outperformed other models, potentially serving as a valuable tool in clinical settings to aid in the rapid diagnosis of DF.
Motivated by Design: A Codesign Study to Promote Challenging Misinformation on Social Media
Authors: Gurgun Selin , Arden-Close Emily , Phalp Keith , Ali Raian
Published: October 2024

The spread of misinformation on social media is a critical issue. One potential solution to mitigate the spread is user corrections; however, users often refrain due to various concerns. Leveraging the established influence of user interface design (UID) on how user interact with and respond to misinformation, this study investigates how user interface features can be designed to motivate users to challenge misinformation. It is aimed at gaining insights into users’ needs and UID requirements that encourage this behaviour. We conducted four codesign sessions with 18 social media users (age range 20–60 years M = 39.1; 10 female and 8 male). We applied the unified theory of acceptance and use of technology (UTAUT) as a theoretical framework and analysed our data based on the core constructs of this framework: performance expectancy, effort expectancy, social influence, and facilitating conditions. Our findings reveal four design considerations: creating secure and supportive environments, facilitating informed discussions through easy confrontation and access to reliable resources, leveraging recognition and social proof, and user support infrastructure. We also identified specific design elements with users, including indirection, semianonymity and privacy, simplicity, one‐click challenging, easy access to reliable sources, recognition, displaying social proof, and platform support. These elements are aimed at reducing social discomfort and making the process of correcting misinformation more approachable for users. Our findings offer actionable insights for social media platform designers to reduce the spread of misinformation by creating environments that encourage constructive dialogues and allow users to challenge misinformation without fear of conflict.
Early‐Life Risk Factors and Clinical Features of Food Allergy Among Thai Children
Authors: Thongsukkaeo Samkhwan , Suksawat Yiwa
Published: October 2024

Background: Food allergy affects 1%–10% of children under five worldwide, with genetic and early‐life factors playing a primary role. Reported factors include a family history of allergic diseases, personal atopic dermatitis, cesarean section, dietary restrictions during pregnancy and lactation, and the timing of introducing solid foods. This study was aimed at identifying various factors associated with food allergy and evaluate each food allergy’s clinical features.

Methods: We conducted a case–control study with a participant ratio of 1:2 between cases and controls. Data were gathered from both groups of participants, and questionnaires included living area, sex, and natal history (birth details, maternal diet during pregnancy and breastfeeding, feeding history during infancy, family history of atopic diseases, and household smoking).

Results: All 72 cases with food allergy and 145 controls were included in the study. Term birth comprised a protective factor for developing food allergy (adjusted odds ratio [aOR] 0.213, p value 0.022). In contrast, personal atopic dermatitis (aOR 20.097, p value 0.001) and a family history of allergic disease constituted risks (aOR 3.183, p value 0.002). Food allergy was unrelated to cesarean section, low birth weight, dietary restrictions during lactation and pregnancy, exclusive breastfeeding, or the early introduction of complementary foods. The three most common food allergens were egg white (40.2%), wheat (34.7%), and cow’s milk (30.5%), respectively.

Conclusions: In this study, risk factors associated with food allergy comprised a personal history of atopic dermatitis and a family allergic disease, which may be used as predictive factors for developing food allergy among Thai children.
Bus Arrival Time Prediction Based on the Optimized Long Short‐Term Memory Neural Network Model With the Improved Whale Algorithm
Authors: Zhang Bing , Tang Lingfeng , Zhou Dandan , Liu Kexin , Xue Yunqiang
Published: October 2024

Accurate prediction of bus arrival time is essential to achieve efficient bus dispatch and improve bus trip sharing rate. This article proposes using the improved whale optimization algorithm–long short‐term memory (IWOA–LSTM) model to predict bus arrival times and improving the whale algorithm by optimizing the hyperparameters of the LSTM model, so that the advantages and disadvantages of the whale algorithm and the LSTM model can complement each other, thus enhancing the robustness of the model. Initially, the bus arrival process and its associated influencing factors are analyzed, with certain factors being quantified to serve as input features for the prediction model. After processing the GPS data of the No. 220 bus in Nanchang, Jiangxi, China, the proposed prediction model is analyzed and validated using an example and compared with other prediction models. The results show that the IWOA–LSTM prediction model has the best‐fitting effect between the predicted values and actual values in all time periods. Its MAPE, RMSE, and MAE have been reduced by at least 9.47%, 12.77%, and 8.93%, respectively, and the overall R2 has been improved by at least 10.65%. These results indicate that the model has the best predictive performance.
The Efficacy and Safety of Low‐Intensity Pulsed Ultrasound Therapy for the Treatment of Peyronie’s Disease (A Report of 106 Cases): A Retrospective Study
Authors: Yu Lei , Pan Yu , Wang Hongqiang , Guo Xiaojing , Fu Ling , Wang Peitao , Gao Yaowu , Jing Shaoping , Li Shenqian , Zhang Kaishu , Li Qiang , Wang Hanshu , Jing Tao
Published: October 2024

Objective: To evaluate the efficacy and safety of low‑intensity pulsed ultrasound (LIPUS) for the treatment of Peyronie’s disease (PD).

Methods: To analyze the treatment effect of 106 PD patients, 56 patients treated with LIPUS as therapy group and 50 patients with oral L‐carnitine and vitamin E as control group. 56 patients were treated with LIPUS three times per week for four consecutive weeks (12 times totally). Penile erectile pain, erectile function, the size of penile plaques, and penile curvature were assessed for all of these 106 PD patients before treatment and at 4‐ and 24‐week follow‐ups to evaluate the treatment effect. Erectile pain was assessed by visual analog scale (VAS), while erectile function using the International Index of Erectile Function scale (IIEF‐5), and penile plaque size was measured by ultrasound. After alprostadil was injected into the penile body to induce a full erection, the penis was measured with a protractor at the maximum bending degree.

Results: After 4‐weeks follow‐up, the VAS scores of the LIPUS group were significantly lower than the baseline (P < 0.01), while no significant differences were found in the control group (P > 0.05). After 24‐weeks follow‐up, the VAS scores of the control group reduced significantly but also were significantly higher than the LIPUS group (P < 0.01). The IIEF‐5 scores of the LIPUS group were significantly higher than the baseline (P < 0.01). While the IIEF‐5 scores of the control group showed no significant differences both after 4‐ and 24‐weeks follow‐up (P > 0.05). The penile plaque size showed no significant differences after 4‐ and 24‐weeks follow‐up both in the LIPUS group and the control group (P > 0.05). The penile curvature degrees showed a significant reduction after 4‐weeks follow‐up in LIPUS group (P < 0.01). While the penile curvature degrees of the control group showed no significant differences both after 4‐ and 24‐weeks follow‐up (P > 0.05). No complications, such as penile pain, hematoma, or skin ecchymosis, were observed during the entire process of LIPUS treatment, nor did they appear during follow‐up.

Conclusions: In patients with acute PD, LIPUS could relieve erectile pain and improve erectile dysfunction. Moreover, it appears not to reduce plaque size and not to have a clinically significant effect on penile curvature. Furthermore, LIPUS also shows a good safety profile during the management of PD and should be recommended for the clinical management of PD.
Vascular Complications and Bleeding After Transfemoral TAVR With Surgical Versus Percutaneous Approach: A Contemporary Prospective Study
Authors: Dardaillon Annick Astié , Akodad Mariama , Huet Fabien , Meunier Pierre Alain , Gandet Thomas , Macia Jean-Christophe , Schmutz Laurent , Targosz Frederic , Roubille François , Steinecker Matthieu , Lattuca Benoit , Robert Pierre , Berdeu Jean-Michel , Cayla Guillaume , Leclercq Florence
Published: October 2024

Background: Percutaneous (PC) access has become the first‐line strategy for transfemoral (TF) transcatheter aortic valve replacement (TAVR), but surgical cutdown (SC) may be required in selected and usually challenging cases. We aimed to compare the efficacy and safety of SC versus echo‐guided PC approach in TF TAVR.

Methods: Between November 2019 and December 2020, all patients undergoing TF TAVR were included in a prospective study. The choice of PC or SC strategy was left at operators’ discretion, according to predictable access site difficulties. The primary endpoint included all vascular complications (VCs) and all bleeding at 1‐month follow‐up according to Valve Academic Research Consortium‐2 criteria.

Results: Among 225 patients, 166 (73.8%) and 59 (26.2%) underwent PC or SC approach, respectively. Median age was 82 years, EuroSCORE 2 was higher (p = 0.02), and peripheral arterial disease (p < 0.001) was more common in the SC group. Femoral access was successful in 100% in the SC group vs 97.6% in the PC group (p = 0.2). The primary endpoint was reached in 32 patients (14.2%), 5 (8.4%) in the SC group and 27 (16.2%) in the PC group (p = 0.1) with low incidence of major VC in both groups (1.7% vs 3% in the SC and PC groups respectively, p = 0.6).

Conclusion: Despite a higher risk profile of patients in the SC group, SC and PC approaches appear safe and effective in TF TAVR, with a high success rate. SC may be considered when vascular access is anticipated as complex, but this strategy might be confirmed in a randomized study.

Trial Registration: ClinicalTrials.gov identifier: NCT03865043
Structural Neuroimaging and Molecular Signatures of Drug‐Naive Depression With Melancholic Features
Authors: Yuan Lijin , Chu Zhaosong , Chen Xianyu , He Mengxin , Lu Yi , Xu Xiufeng , Shen Zonglin
Published: October 2024

Objectives: Melancholic depression (MD) is a common subtype of major depressive disorder (MDD). It is difficult to treat because its neurobiological basis is poorly understood. Therefore, to investigate whether MD patients have any structural changes in gray matter (GM) and the molecular foundation of these changes, we combined voxel‐based morphometry (VBM) analysis with neurotransmitter system‐derived mapping from public data.

Methods: 137 drug‐naive MDD patients and 75 healthy controls (HCs) were recruited for structural magnetic resonance imaging. The imaging results were analyzed using VBM analysis. MDD patients were then divided into MD and nonmelancholic depression (NMD) subgroups according to their scores on the Montgomery–Asberg Depression Rating Scale (MADRS) and the Hamilton Depression Rating Scale. Next, we analyzed the spatial correlation between the changes in the gray matter volume (GMV) maps and the neurotransmitter receptor/transporter protein density maps provided by the JuSpace toolbox.

Results: Compared to HCs, patients with MD had significant GMV reduction in the bilateral hippocampus, bilateral thalamus, right amygdala, and right posterior cingulate cortex (PCC)/precuneus. Compared to patients with NMD, MD patients had significant GMV reduction in the bilateral PCC/precuneus and lateral occipital cortex. Moreover, compared to HCs, changes in GMV introduced by MD were spatially associated with the serotonin transporter, cannabinoid receptor, and μ‐opioid receptor. Compared to NMD patients, changes in GMV introduced by MD were spatially associated with the vesicular acetylcholine transporter.

Conclusion: The present study discovered abnormal GMV alterations in patients with subtypes of depression. We also found a series of neurotransmitter receptors that may be associated with the alterations. The findings of the current study may provide a more comprehensive understanding of the molecular mechanisms underlying the structural abnormalities in subtypes of depression and potentially offer new insights into developing new therapeutic strategies.
Supplementary Note from Mutational Landscape and Patterns of Clonal Evolution in Relapsed Pediatric Acute Lymphoblastic Leukemia
Authors: Waanders Esmé , Gu Zhaohui , Dobson Stephanie M. , Antić Željko , Crawford Jeremy Chase , Ma Xiaotu , Edmonson Michael N. , Payne-Turner Debbie , van de Vorst Maartje , Jongmans Marjolijn C.J. , McGuire Irina , Zhou Xin , Wang Jian , Shi Lei , Pounds Stanley , Pei Deqing , Cheng Cheng , Song Guangchun , Fan Yiping , Shao Ying , Rusch Michael , McCastlain Kelly , Yu Jiangyan , van Boxtel Ruben , Blokzijl Francis , Iacobucci Ilaria , Roberts Kathryn G. , Wen Ji , Wu Gang , Ma Jing , Easton John , Neale Geoffrey , Olsen Scott R. , Nichols Kim E. , Pui Ching-Hon , Zhang Jinghui , Evans William E. , Relling Mary V. , Yang Jun J. , Thomas Paul G. , Dick John E. , Kuiper Roland P. , Mullighan Charles G.
Published: October 2024

Supplementary results, figures, and table notes
Supplementary Fig. 3 from Peroxisome Proliferator–Activated Receptor δ Suppresses the Cytotoxicity of CD8+ T Cells by Inhibiting RelA DNA-Binding Activity
Authors: Cen Bo , Wei Jie , Wang Dingzhi , DuBois Raymond N.
Published: October 2024

Supplementary Fig. 3 shows RelA and PPARδ bind to the same DNA fragments of IFNγ, granzyme B, or perforin gene promoters in human CTLs.
Figure 4 from Peroxisome Proliferator–Activated Receptor δ Suppresses the Cytotoxicity of CD8+ T Cells by Inhibiting RelA DNA-Binding Activity
Authors: Cen Bo , Wei Jie , Wang Dingzhi , DuBois Raymond N.
Published: October 2024

PPARδ inhibits RelA/p50 DNA-binding activity. A–C, DNA-binding activity of RelA and p50 in the nucleus of Ppard+/+ and Ppard−/− murine CTLs (A), human CTLs transfected with plasmid expression GFP or human PPARδ (B), human CTLs treated with indicated concentrations of GW501516 (GW; C). Data (mean ± SD) represent three independent experiments with similar results. *, P < 0.05. D, Reporter activity of an NF-κB luciferase reporter in human CTLs transfected with a plasmid expressing PPARδ, treated with GW501516, or treated with PPARδ siRNA. E, Western blot expression of PPARδ and other indicated proteins in the nuclear extracts of human CTLs treated with GW501516 (GW). F, Binding of RelA to the promoters of Ifng, Gzmb, or Prf1 gene in human CTLs treated with PPARδ siRNA in CHIP–qPCR assays. Primer pairs amplify regions close to the transcription start sites using a control IgG antibody or antibody against RelA or acetyl-Histone H3 (Lys27). CHIP–qPCR data from these samples using the 3′-UTR primer pairs are shown. Data (mean ± SD) represent three independent experiments with similar results. *, P < 0.05.
Figure S5 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Authors: Iwanaga Ritsuko , Yamamoto Tomomi M. , Gomez Karina , Nguyen Lily L. , Woodruff Elizabeth R. , Post Miriam D. , Mikeska Railey G. , Danis Etienne , Danhorn Thomas , Boorgula Meher P. , Mitra Siddhartha S. , Marjon Nicole A. , Bitler Benjamin G. , Brubaker Lindsay W.
Published: October 2024

In vivo modeling with loss of CBX2 expression and Nanostring pathway analysis
Figure S1 from Tumor-Intrinsic Activity of Chromobox 2 Remodels the Tumor Microenvironment in High-grade Serous Carcinoma
Authors: Iwanaga Ritsuko , Yamamoto Tomomi M. , Gomez Karina , Nguyen Lily L. , Woodruff Elizabeth R. , Post Miriam D. , Mikeska Railey G. , Danis Etienne , Danhorn Thomas , Boorgula Meher P. , Mitra Siddhartha S. , Marjon Nicole A. , Bitler Benjamin G. , Brubaker Lindsay W.
Published: October 2024

CBX2 binds to promoter regions of cytokine genes and CBX2 is significantly associated with immune signatures.
Artificial Intelligence-Driven Skin Aging Simulation as a Novel Skin Cancer Prevention
Authors: Gantenbein Lorena , Cerminara Sara Elisa , Maul Julia-Tatjana , Navarini Alexander A. , Maul Lara Valeska
Published: October 2024

Introduction: Skin cancer, a prevalent cancer type among fair-skinned patients globally, poses a relevant public health concern due to rising incidence rates. Ultraviolet (UV) radiation poses a major risk factor for skin cancer. However, intentional tanning associated with sunburns remains a common practice, notably among female adults. Appropriate prevention campaigns targeting children and adolescents are needed to improve sun protection behavior particularly in these age groups. The aim of our study was to investigate if an AI-based simulation of facial skin aging can enhance sun protection behavior in female adults. Methods: In this single-center, prospective, observational pilot study at Department of Dermatology at the University Hospital of Basel, we took photographs of healthy young females’ faces with a VISIA-CR camera (Version 8.2; Canfield Scientific Inc., Parsippany, NJ, USA) between February and March 2021. Digital images were performed in three angles (straight, left 45°, and right 45°). All participants received an AI-based simulation of their facial skin with continuous aging to 80 years. A newly created anonymous questionnaire capturing participants’ sociodemographic data and also tanning and sun protection behavior was completed in pre- and post-aging simulation. To observe long-term effects, a 2-year follow-up was conducted between March and April 2023. Results: The 60 participants (mean age 23.6 ± 2.5 years) evaluated the importance of sun protection significantly higher after skin aging simulation with VISIA-CR camera (p < 0.0001; 95% CI: 8.2–8.8). Post-intervention, 91.7% (55/60) of the females were motivated to reduce UV exposure and to intensify UV protection in the future since the individual UV-dependent risk was perceived significantly higher (p < 0.001; 95% CI: 5.9–6.7). At 2-year follow-up, 96% (24/25) indicated persistent effort reducing UV exposure. The preference for SPF 50+ sunscreen increased to 46.7% (28/65) directly after the skin aging simulation and continued to rise up to 60.0% (15/25) after 2 years. Conclusions: Our data emphasize the potential of AI-assisted photoaging interventions to enhance motivation for UV protection in the short and the long term. We encourage that different age and gender groups are addressed in a personalized, generation-specific manner with the appropriate media and by considering the Hawthorne effect. Campaigns with visual AI support can improve the intent of cancer-preventative behavior.
Mapping anthropogenic impacts on natural resources in the northeastern Sahara region: A case study on the past 26 years over the Ziban region (Algeria)
Authors: Kendri Samira , Masmoudi Ali , Pirotti Francesco , Abdennour Mohamed Amine
Published: October 2024

The objective of this research was to map and analyze anthropogenic impacts on natural resources in the Ziban region of Algeria using remote sensing imagery from 1995 to 2021. Medium-resolution satellite images from Landsat TM and OLI were utilized, with image processing techniques such as colorful compositions and supervised classification to map land cover. Seven land use and land cover (LULC) classes were identified, distinguishing human-derived categories (palm, agriculture, urban, and greenhouses) from natural surfaces (nebka, natural vegetation, and bare soil). The results indicated significant increases in human-driven elements like palm, urban areas, agriculture, greenhouses, and nebka, and notable decreases in natural components like vegetation and bare soil. Specifically, the rate of change (Tc) showed increases of 0.9% for palm, 1.14% for urban, 1.55% for agriculture, 1.91% for greenhouses, and 9.37% for nebka, and decreases of -3.68% for vegetation and -11.19% for bare soil. These findings highlight the impact of agricultural policies, population growth, and natural and human-induced conditions on natural resources. Palm areas remained largely unchanged due to state policies, whereas agriculture saw significant conversions to bare soil and palm. Vegetation declined substantially due to adverse climatic conditions and agricultural expansion. Nebka and urban areas had moderate to high conversion rates, and bare soil saw notable changes due to sand movement and urban/agricultural development. The study highlights the role of remote sensing and land cover analysis in managing natural resources sustainably, considering factors like agricultural policies and population growth. It emphasizes developing data-driven strategies for effective land use and management.
Biomagnetismo medicinal na hepatite autoimune: um estudo de caso
Authors: Azevedo Caroline , Cunha Simone da Silva , Oliveira Mariley Wanderley de , Rambo Angela Mara , Bossa Adriane Viapiana
Published: October 2024

Autoimmune Hepatitis (AIH) is a chronic and severe inflammatory disease of the liver caused by loss of tolerance to hepato-specific autoantigens, and the identification is made by applying diagnostic scores. It is characterized by an increase in serum aminotransferases - aspartate aminotransferase (AST) and alanine aminotransferase (ALT), immunoglobulin G, autoantibodies, where triggering agents appear that confuse the immune system that recognizes components of some hepatocytes as antigens. In this study, Medicinal Biomagnetism (BM) was applied, which is a treatment method developed by Dr. Isaac Goiz Durán (1941-2021), whose technique consists of applying a pair of medium intensity magnets -1,000 to 7,500 Gauss, with opposite polarities that resonate with each other and aim to depolarize the dysfunction magnetic field called Biomagnetic Pair (BMP). The magnetic resonance generated by the static magnetic fields, identifies and restores the electron spin of the biochemical elements that support the hydrogenion potential (pH) distortion. The objectives os this study were: to investigate whether there was any change in the clinical and laboratory signs of the patient undergoing BM and to investigate the applicability of the technique in her clinical evolution; compare possible changes in saliva pH before and after therapy; observe whether there were changes in liver enzymes during the treatment period; find out whether during the treatment the hepatologist indicated a reduction in the dosage of the medication in view of the clinical/laboratory improvement; and if there was an improvement in symptons. Through the analysis of the medical records, it was observed that the BM tecnique made it possible to improve the clinical/laboratory status of the patient with AIH.
Abstract 09: Leucine Rich Repeat Containing 8A Anion Channels Modulate Vascular Reactivity in ApoE Knockout Mice
Authors: Choi Hyehun , Panja Sourav , Nguyen Hong , Lamb Fred
Published: October 2024

Volume-regulated anion channels (VRACs) are comprised of Leucine Rich Repeat Containing 8 (LRRC8) family proteins which include LRRC8A, B, C, D and E. We have proposed that these channels may allow influx of extracellular superoxide to drive inflammation and modulate cultured vascular smooth muscle cell (VSMC) inflammation. We previously demonstrated that LRRC8A knockdown or inhibition inhibits tumor necrosis factor alpha (TNFα)-induced inflammation. Mesenteric arteries from male smooth muscle-specific LRRC8A knockout (KO) mice displayed enhanced responsiveness to both endothelium-dependent (acetylcholine, ACh), and independent (sodium nitroprusside, SNP) vasodilators compared to vessels from WT mice. KO vessels were also protected from vascular dysfunction induced by TNFα in tissue culture (48hr exposure). We hypothesized that reduced VSMC inflammation in LRRC8A KO mice would protect vascular function in ApoE knockout (ApoE
-/-
). We created ApoE
-/-
mice lacking LRRC8A only in VSMCs and assessed vascular reactivity of isolated mesenteric arteries by wire myography in female and male WT, Heterozygous (HET) and KO tissues after 14 weeks of high fat diet. WT vessels displayed enhanced in contraction compared to LRRC8A KO or HET (male PE max contraction of KCL (%), WT 177 ± 4.6 vs. KO 152 ± 3.3 or HET 153 ± 3.0,
p
< 0.05, n=6-7). Relaxation responses to ACh were increased in female KO or HET compared to WT (ACh LogEC
50
, WT -7.2 ± 0.09 vs. KO -7.5 ± 0.09 or HET -7.4 ± 0.07,
p
< 0.05, n=5-6). SNP-mediated relaxation were increased in male KO vessels compared to WT (SNP LogEC
50
, WT -8.1 ± 0.06 vs. KO -8.3 ± 0.06,
p
< 0.05, n=7). These results demonstrate that loss (KO) or reduction (HET) of LRRC8A expression preserves vascular function in the face of the inflammatory response induced by hypercholesterolemia in ApoE
-/-
mice. These channels may represent a novel therapeutic target for preservation of vascular function and this may only require partial inhibition of current.