bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing TNM Staging in Breast Cancer: A Hybrid Approach with CNN, Edge Detection, and Self-Organizing Maps for Improved Accuracy
Authors: Ajlouni Naim , Özyavaş Adem , Ajlouni Firas , Takaoğlu Mustafa , Takaoğlu Faruk 
Published: August 2024

Abstract

Breast cancer remains a leading cause of mortality among women globally, underscoring the urgent need for improved diagnostic and staging techniques to enhance patient outcomes. This study aims to automate the TNM staging of breast cancer using a hybrid approach that integrates Convolutional Neural Networks (CNNs), edge detection methods, and Self-Organizing Maps (SOMs). Utilizing the Duke Breast Cancer MRI dataset, which provides detailed MRI scans crucial for accurate tumor characterization, the research addresses the limitations of traditional TNM staging, which often relies on manual interpretation by radiologists and can lead to inconsistencies and inaccuracies. Our approach combines CNNs with advanced edge detection algorithms and SOMs to automate and enhance the accuracy of breast cancer staging. The hybrid model effectively identifies and delineates tumor boundaries and critical anatomical features, offering a more reliable and objective evaluation. Notably, this method improves accuracy from 93% with conventional CNN models to 98%, representing a significant advancement in precision. This improvement not only provides more accurate diagnoses but also enables more personalized and effective treatment plans. For patients, this enhanced accuracy translates to better prognostic assessments and tailored treatments, potentially leading to improved outcomes and reduced likelihood of overtreatment or under treatment. For medical staff, the improved accuracy reduces the likelihood of misdiagnoses and enhances workflow efficiency by minimizing manual interpretation, thus alleviating some of the burdens associated with cancer staging. The model's performance is optimized through various testing methods and statistical evaluations, validating its stability and reliability. The integration of edge detection and SOMs captures comprehensive information, prevents overfitting, and provides valuable insights into data clustering. This combined approach supports personalized medicine by ensuring treatments are customized to individual patient characteristics, ultimately contributing to better survival rates and quality of life for patients.
Deciphering Complex Regions within the Human Genome and Unraveling Their Critical Biomedical Regulatory Functions
Authors: Du Duo , Li Ming , Zhu Chaoyu , Li Xuan , Wu Feizhen , Zhang Wen , Cheng Xunjia , Zhong Fan , Liu Lei 
Published: August 2024

Abstract

Background: Nuclear genomic DNA plays a crucial role in individual development and phenotype determination. The genetic landscape within populations exhibits significant heterogeneity, contributing to diverse human traits. Current studies of human genome heterogeneity often focus on specific segments of high-frequency phenotype-associated sequences or structurally complex regions. Therefore, to overcome the limitations of previous studies and more directly explore population heterogeneity, it is essential to study the entire genome rather than focusing only on known phenotype-associated regions.
Results: Using set theory, we have clearly defined Complex Regions (Complex_Region) by integrating pan-genome datasets, covering about 8.1% of the human genome. These regions exhibit high sequence diversity and nonrandom long continuous fragments (≥450kb), thus reflecting population genetic complexity. Our enrichment analysis revealed that genes within Complex_Region are primarily involved in immunity and metabolism, indicating chromosome-specific functional enrichment. Notably, immune genes are mainly located on chromosomes 6 and 19, which are closely associated with disease occurrence. Moreover, these regions are enriched for human phenotype-related signals and tumor somatic mutations, providing novel insights for large-scale cohort studies. We also detected ancient viral sequences, particularly ~9.47 kb human endogenous retroviruses (HERV) insertion sequence NC_022518, which is diverse in humans but remains conserved across primates, to be implicated in regulating bodily functions and various diseases.
Conclusions: Our study highlights the biomedical importance of Complex_Region by revealing associations among genotypes, environment, and phenotypes. This enhances our understanding of life regulation and phenotype shaping, highlighting the role of these regions in immunity, metabolism, and disease association.
Characterize of Drought Events Using Decile, SPI and RDI in Southeast of Ethiopia
Authors: Kemal Kedir , Mekonnen Zenebe , Molla Moges , Shimelis Abebaw 
Published: August 2024

Abstract

Drought characterization can be used to devise an effective early warning system as a key component to building drought preparedness plan that manage drought impacts. The aim of this study was to characterize the drought events, particularly those of meteorological and agricultural events happened for long period that has been the predominant types to affect the communities. Conventionally, regional drought analysis is done based on drought indices for the identification of drought intensity, frequency, duration and areal extent across southeast Ethiopia. The indices used for the study were Precipitation Deciles (PD), Standardized Precipitation Index (SPI) and Reconnaissance Drought Indexes (RDI) for the period of 1987 to 2018. The spatial extent of drought in the study area has been interpolated by inverse distance weighted method using the spatial analyst tool of ArcGIS. Most of the studied stations experienced drought event in 1991/1992, 1998/1999, 2001/2002, 2010/2011 and 2011/2012. The results point to 2010/2011 as a year of exceptionally widespread drought among the other. To this end, having information in the intensity, frequency, duration and areal extent of drought needed in water conservation policies can help bridge the gap between water availability, supply, and demand of drought affected areas.
Controllable Synthesis of Nonlayered Ultrathin Mn3O4 with High Dielectric Constant
Authors: Liu Wei , Yuan Jiashuai , Jian Chuan-yong , Shang Zhihui , Yao Yu , Wang Bicheng , Li Yixiang , Wang Rutao , Fu Zhipeng , Li Meng , Hong Wenting , He Xu , Cai Qian 
Published: August 2024

Abstract

Two-dimensional (2D) materials have been identified as promising candidates for future electronic devices. However, high dielectric constant (κ) materials, which can be integrated with 2D semiconductors, are still very rare. This study employed a hydrate-assisted thinning chemical vapor deposition (CVD) technique combined with a strategy of minimizing substrate lattice mismatch, achieving precise control over the growth kinetics, enabling the controllable array growth of ultrathin manganese oxide (Mn3O4), demonstrating an extraordinary dielectric constant of 237, an equivalent oxide thickness (EOT) as low as 0.57 nm, and a breakdown field strength (Ebd) exceeding 10 MV/cm, marking it as a superior gate dielectric material. MoS2 field-effect transistors (FETs) integrated with ultrathin Mn3O4 through mechanical stacking method operate under ultra-low voltages (<1 V), achieving a near 108 Ion/Ioff ratio and a subthreshold swing (SS) as low as 84 mV/dec. The MoS2 FET exhibit nearly zero hysteresis (~5 mV) and an extremely low drain-induced barrier lowering (~20 mV/V). This work further expands the family of 2D high-κ dielectric materials and provides a feasible exploration for the epitaxial growth of single-crystal thin films of non-layered materials.
Prevalence of Posttraumatic Stress Disorder and Associated Psychiatric Disorders in Earthquake Victims After the Elazig Earthquake in 2020
Authors: MOLU Esengul , Gurok Mehmet Gurkan , Tabara Muhammed Fatih , Atmaca Murad 
Published: August 2024

Abstract

Objective:This study investigated the prevalence of PTSD and accompanying mental illnesses in Elazig Earthquake survivors, aiming to identify risk factors for PTSD and the prevalence of depression and anxiety.
Method:The sample comprised 320 earthquake survivors aged 18 and above who experienced the earthquake. The participants completed a sociodemographic data form, Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Clinician-Administered PTSD Scale (CAPS), and Traumatic Experiences Checklist (TEC).
Results:The study found that the prevalence of PTSD among earthquake victims was 13.1%. Additionally, 24.37% of participants experienced moderate to severe depression, while 25.32% had moderate to severe anxiety.
Conclusion:Identifying potential risk factors for mental illness development after an earthquake and providing targeted psychiatric support to at-risk individuals can help prevent the onset of PTSD and other psychiatric disorders.
Enhancing Convergence of Optimization Algorithms for Logistic Regression: A Breast Cancer Diagnosis Application
Authors: Borbor Ariana , Herrera Priscilla , Ashraf Ahmed , Delgado Erwin , Liut Michael , Suarez Carlos Anibal 
Published: August 2024

Abstract

This paper explores strategies to enhance the convergence of Newton and Trust Region methods, which are employed to obtain optimal solutions for the optimization problems underlying the Logistic Regression technique. The optimization problems consist of maximizing the likelihood function and minimizing the sum of squared errors. These are addressed using the mentioned gradient-based algorithms. Improvements include using a local search heuristic to find a good initial solution and determining the optimal learning rate at each iteration. The heuristic-generated initial points significantly improve the convergence rate of these gradient-based methods. Additionally, instead of using a fixed learning rate or step size, the optimization process dynamically computes these parameters using the Golden Search method, ensuring a more adaptive and efficient search strategy. Extensive numerical experiments and a detailed case study validate the effectiveness of this approach.
Precise and interpretable neural networks reveal epigenetic signatures of aging in youth across health and disease
Authors: Martínez-Enguita David , Hillerton Thomas , Åkesson Julia , Kling Daniel , Lerm Maria , Gustafsson Mika 
Published: August 2024

Abstract

DNA methylation age clocks are powerful tools for measuring biological age, providing insights into aging risks and outcomes beyond chronological age. While traditional clocks are effective, their interpretability is often limited due to their dependence on small, potentially stochastic sets of CpG sites. We propose that the reliability of epigenetic age estimation models should stem from their ability to detect comprehensive and targeted aging signatures. Here we introduce NCAE-CombClock, a neural network regressor that improves age prediction accuracy in large datasets by integrating methylation embeddings with CpG sites. Additionally, we developed explainable neural networks for robust age classification across adolescence and young adulthood. Epigenetic aging signatures identified at single-year resolution from these models were enriched in developmental, immune, and metabolic processes. We showcase the utility of our approach by exploring the biological mechanisms underlying the altered pace of aging observed in pediatric Crohn's disease. Our models offer broad applications in personalized medicine and aging research, providing a valuable resource for interpreting aging trajectories in health and disease.
Single Axis Solar Tracker using PIC Microcontroller and Light Dependent Resistors
Authors: Tirones Sylvester 
Published: August 2024

Abstract

The use of renewable energy for electricity generation has become increasingly demanding in remote locations and off-grid areas around the globe. Harnessing renewable energy such as the solar radiation from the sun to produce electrical energy enables the decrease in the cost of the energy demands and at the same time, contributes to sustainability and mitigates global warming and its effect. Since sunlight is natural and abundant, focussing on the development of managing the harvesting of this energy is quite imperative. In this paper, we shall present a design that integrates the use of a solar photovoltaic system along with a simple single-axis solar tracking system that aims at improving the energy absorption by the solar panel by aligning the panel perpendicular to the sunlight. The simple design consists of multiple stationary LDRs used as sensors for sunlight detection assembled at a fixed position and a single rotational LDR attached to the movable solar panel to match the sun's position with the stationary LDRs. The results show that the proposed single-axis solar tracker presented in this paper is economical and can increase the power output of the solar PV panel. The model can be projected on a bigger scale and targets domestic electricity production in rural locations.
Uma Sequência de Ensino com característica Interdisciplinar, envolvendo matemática financeira e meio ambiente
Authors: Ribeiro Lucivânia da Silva Costa , Oliveira Tamiles da Silva , Correia Diná da Silva , Santos Elisângela Ribeiro dos , Viana Geovânia Souza 
Published: August 2024

Este relato tem como objetivo apresentar uma experiência interdisciplinar vivenciada por uma professora de matemática e uma professora de ciências envolvidas em um processo formativo, coordenado pelo Grupo de Pesquisa em Educação Matemática, Estatística e em Ciências (GPEMEC), com uma proposta de desenvolvimento de uma sequência de ensino e que, de forma colaborativa, por meio de quatro encontros, se tornou possível executar parte das suas ações propostas, cuja abordagem utilizada foram estudos dos textos de autores que pesquisam temas como Práticas Pedagógicas e Interdisciplinaridade. Os encontros foram organizados a partir das reflexões de leituras prévias destes textos, bem como discussão acerca da realidade do município, quanto à coleta seletiva e venda de materiais recicláveis, e o ganho financeiro dos catadores deste material. A matemática financeira e a preservação do meio ambiente foram os temas curriculares trabalhados na elaboração e desenvolvimento desta sequência. Os dados parciais coletados, se deu pelo diálogo dos alunos com as pessoas envolvidas na coleta seletiva de materiais recicláveis, bem como a reflexão das duas professoras. Os resultados apontam que esta ação pedagógica, pode contribuir com uma educação em um contexto de Interdisciplinaridade, a partir de uma ação de cunho social, promovendo uma aprendizagem para os estudantes além da sala de aula.
Analisis Kepuasan Pengguna Gojek dengan Metode Kuantitatif Multimodel
Authors: Yulianing Tyas Sasmi Hidayatul , Muftikhali Qilbaaini Effendi 
Published: August 2024

Gojek is the first start-up company in Indonesia to have decacorn status based on CB-Insight's records. With Go-Jek's achievement status being the background factor for satisfaction with Go-Jek application users. This research aims to identify factors that influence Gojek customer satisfaction with application use. In this research, quantitative methods using the EUCS, TAM and Delone and McLean (Multi Model) models were used to identify the independent variables, while the dependent variable was customer satisfaction. The dependent variables in this research consist of Content, Accuracy, Format, ease of use, Timeliness, Perceived Usefulness, System Quality, Information Quality and Service Quality. Quantitative methods were used in this research, based on the calculation results of the GeSCA application. The R-squared value of the model proposed in this study is 0.6922. This means that the independent variables in the model can explain the customer satisfaction variable by 69.22% and the remaining 31.78% is explained by other independent variables that are not in the model. Based on the results of the analysis, it was found that 9 variables had an influence on the customer satisfaction variable, but there was 1 variable that had a significant influence (Service Quality). Conclusion of this research show that the variables Content, Accuracy, Format, ease of use, Timeliness, Perceived Usefulness, System Quality, Information Quality and Service Quality have an influence on user satisfaction.
Potential Roles of Long Non-coding RNAs in the Pathogenesis of
Periodontitis: Inflammation Response, Immune Infiltration, Collagen
Fibers Synthesis, and Bone Remodeling
Authors: Yuan Shuai , Chu Yinglin , Liu Fei , Xiao Yan 
Published: August 2024

Background:
It is evident that long non-coding RNAs (lncRNAs) are implicated
in the pathogenesis of periodontitis. However, the detailed functional mechanisms
remain unknown.

Objective:
This study aimed to elucidate the pathogenic mechanisms of lncRNAs in periodontitis
by investigating their regulation of protein-coding gene expression.

Method:
Human Gingival Fibroblasts-1 (HGF-1) were stimulated with 5 μg/mL of
Lipopolysaccharide (LPS) for 24 hours to construct the periodontitis cell model. qRTPCR
and western blot analyses were carried out to determine mRNA and protein levels
of genes induced by LPS or involved in the inflammatory response. Cytokine levels and
inflammatory proteins were assayed using ELISA. Transcriptome sequencing and analysis
were conducted to reveal the expression signatures of lncRNAs. DESeq2 (v1.4.5)
was used to analyze differentially expressed genes. Gene function enrichment was carried
out using Phyper. AnimalTFDB v3.0 was used to analyze transcription factors involved
in the pathogenesis of periodontitis. Prot\ein domains and families of the target
proteins were identified based on the Pfam protein family database.

Results:
In LPS-treated HGF-1 cells, we detected the secretion of TNF-α and IL-1β,
along with the production of MDA and ROS, indicating that LPS significantly triggered
inflammatory responses and oxidative stress in HGF-1 cells. A total of 15,295 lncRNAs
were detected in both the control (ConT) and LPS-treated groups. We selected 10 significantly
differentially co-expressed lncRNA-coding genes (MIR222HG, SNHG15, SNHG12,
URS00005F6AA3, URS00009C153E, URS0000D57D7F, URS00019A4688,
URS00019AF240, URS00019C6526, and URS0001A00B79) as potential biomarkers
for diagnosing the progression of periodontitis. An interaction network consisting of 2 lncRNA-
encoding genes (MIR222HG and SNHG15) and protein-encoding genes (CBX5,
NUPR1, CHAC1, and MAB21L3) may be involved in the pathogenesis of periodontitis.
The ceRNA network analysis revealed the differentially expressed lncRNAs to be involved
in inflammatory response, immune infiltration, collagen fiber synthesis, and
bone remodeling in LPS-induced periodontitis.

Conclusion:
This study has identified pivotal molecules implicated in the pathogenesis
of periodontitis, including those involved in inflammation regulation, collagen fiber synthesis,
and bone remodeling. Our findings may contribute to explaining how lncRNAs
participate in the pathological process of periodontitis.
A New Perspective on the Molecular Targets, Mechanisms of Action, and Clinical Significance of Ursolic Acid’s Multifaceted Anti-cancer Effects
Authors: Rezaul Islam Md. , Rauf Abdur , Akash Shopnil , Fakir Md. Naeem Hossain , Al-Imran Md. Ibrahim Khalil , Thufa Gazi Kaifeara , Trisha Sadiya Islam , Habiba Umme , Aljohani Abdullah S.M. , Al Abdulmonem Waleed , Iriti Marcello 
Published: August 2024

:
A pentacyclic triterpenoid produced from medicinal herbs, fruits, and vegetables,
Ursolic acid (UA) has pharmacological activity. This review provides a comprehensive
overview of the interactions of UA with molecular targets, its various mechanisms of action,
and its clinical implications in cancer therapy. Numerous studies have been conducted
on the pharmacological effects of UA, and its biological benefits, such as its antiinflammatory,
antioxidant, and anti-cancer activities, have been demonstrated. The study
showed how signaling pathways, such as PI3K/Akt, MAPK, and NF-κB, work together to
control cell death, proliferation, and inflammation. UA effectively treats cancer by interacting
with molecular targets in cell signaling pathways, making it a potent treatment option. It
inhibits tumor cell transformation, limits their reproduction ability, and triggers apoptosis. It
also has been found to inhibit various pro-inflammatory transcription factors and cell cycle proteins, such as
kinases, cytokines, chemokines, adhesion molecules, and inflammatory enzymes. The targets may aid in UA's
chemopreventive and therapeutic benefits by preventing cancer initiation, growth, and metastasis. UA inhibits
cancer cell proliferation by arresting and triggering apoptosis through the cell cycle. It is a promising anticancer
agent with various mechanisms of action. Additionally, it can target multiple signaling pathways and
influence the tumor microenvironment, suggesting its potential as a complementary therapy in cancer treatment.
Further clinical investigations are needed to entirely understand the therapeutic potential of UA and optimize its
application in cancer. This review explores the molecular targets of UA and provides insights into its potential
anticancer activities.
An Efficient Integrated Strategy for Comprehensive Metabolite Profiling of Sakurasosaponin from Aegiceras corniculatum in Rats
Authors: Wang Xiangying , Yang Xiao , Hao Erwei , Xie Jinling , Du Zhengcai , Deng Jiagang , Hou Xiaotao , Wei Wei 
Published: August 2024

Objective:
Sakurasosaponin, a primary bioactive saponin from Aegiceras corniculatum, shows potential
as an anti-cancer agent. However, there is a lack of information on its in vivo metabolism. This study
aims to profile the in vivo metabolites of sakurasosaponin in rat feces, urine, and plasma after oral administration.
An efficient strategy using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass
spectrometry was developed, which combined metabolic prediction, multiple mass defects filtering, and highresolution
extracted ion chromatograms for rapid and systematic analysis.

Methods:
Firstly, a theoretical list of metabolites for sakurasosaponin was developed. This was done by considering
the metabolic pathways of saponins. Next, the multiple mass defects filtering method was employed
to identify potential metabolites in feces and urine, using the unique metabolites of sakurasosaponin as multiple
mass defects filtering templates. Subsequently, a high-resolution extracted ion chromatogram was used to
quickly determine the metabolites in rat plasma post-identification in feces and urine. Lastly, the analysis of
accurate mass, typical neutral loss, and diagnostic ion of the candidate metabolites was carried out to confirm
their structural elucidation, and metabolic pathways of sakurasosaponin in vivo were also proposed.

Results:
In total, 30 metabolites were provisionally identified in feces, urine, and plasma. Analysis of metabolic
pathways revealed isomerization, deglycosylation, oxidation, hydroxylation, sulfate conjugation, glucuronide
conjugation, and other related reactions as the primary biotransformation reactions of sakurasosaponin in vivo.

Conclusion:
The findings demonstrate that the designed research strategy effectively minimizes matrix interference,
prevents the omission of low-concentration metabolites, and serves as a foundation for the discovery
of active metabolites of sakurasosaponin.