bluuebunny/crossref_metadata_2025 huggingface dataset:
Mixture-Based Machine Learning Analysis to Predict Fouling Release Using Insights from Newly Developed Mixture Descriptors
Authors: Ashtari Mahini Rahil , Safaripour Maryam , Khanam Achiya , Casanola-Martin Gerardo M. , Webster Dean C. , Ludwig Simone A. , Rasulev Bakhtiyor
Published: January 2025

The Quantitative Structure-Activity Relationship (QSAR) approach for predicting the biological activity and physicochemical properties of mixtures is gaining prominence, driven by the growing demand for highly engineered materials designed for specific functions. Developing mixture descriptors that effectively capture the intricacies of multi-component materials presents a significant challenge due to their structural complexity. We implemented a series of existing and new mixing rules to drive the mixture descriptors and develop mixture-based-QSAR (mxb-QSAR) models. We evaluated 12 additive mixture descriptors, and a novel non-additive combinatorial descriptor derived from the Cartesian product. These descriptors were used to model the fouling release (FR) property of 18 silicone oil-infused PDMS coating polymers by characterizing the removal of Ulva. linza. Various linear and nonlinear mxb-QSAR models were obtained using these 13 mixture descriptors. The best model, derived from the newly proposed Cartesian-based combinatorial mixture descriptors, employed a decision tree in combination with a two-stage feature importance feature selection. This model achieved a coefficient of determination R2 of 0.987 for both training and test sets, along with a cross-validation Q2 LOO of 0.791. The success of the nonlinear model and combinatorial descriptors underscores the significance of complex relationships among variables, as well as the synergistic effects of the components on fouling release properties.
Effect of Amino Acids on the Synthesis of NiFe2O4/Au Hybrid Nanoparticles
Authors: Nemkova Diana , Saikova Svetlana , Krolikov Anton
Published: January 2025

Hybrid nanoparticles based on a magnetic material and gold are of interest for applications in catalysis, diagnostics and nanomedicine. In this study, the effect of reaction parameters on the reduction of HAuCl4 by different non-sulfur amino acids (glycine, L-serine, L-tryptophan, and L-tyrosine) was determined using the Design of Experiment (DOE) method. The results of the analysis of the regression equations were used to select the conditions and develop a methodology for the preparation of the hybrid magnetic NiFe2O4/Au nanoparticles (NPs) by direct reduction of gold on the magnetic surface using the aforementioned amino acids as the reducing and stabilizing agents simultaneously. The materials were characterized using XRD, TEM, XPS, and Vis/FTIR spectroscopy. The results indicate the successful synthesis of magnetic NiFe2O4/Au nanoparticles with all amino acids used, but the size of the gold crystals, their surface density and the details of the NPs structure (inlaid or a core-shell structure) depend on the amino acid used. The mechanism of the gold deposition on the magnetic core surface and the difference in the effect of various amino acids are discussed. The developed synthesis strategy can be extended to other metal ferrites and iron oxides.
Spatio-Temporal Analysis of Drought with SPEI in the State of Mexico and Mexico City
Authors: Carrillo-Carrillo Mauricio , Ibáñez-Castillo Laura , Arteaga-Ramirez Ramon , Arevalo-Galarza Gustavo
Published: January 2025

Climate change and increased demand for water are causing supply problems in the Mexico City and State of Mexico. The lack of complete and up-to-date meteorological information makes it difficult to understand and analyze climate phenomena such as droughts. Climate Engine (http://ClimateEngine.org) provides decades of climate data to analyze such changes. In this study, CHIRPS and DAYMET were used to calculate SPEI at scales of 1, 3, 6, 9, 12 and 24 months between 1981 and 2023 in the study area. The Mann-Kendall test for precipitation showed no significant change, while maximum and minimum temperatures showed a significant growth trend, which was reflected in the SPEI values. In the last 10 years, droughts have become more frequent and severe in the western part of the State of Mexico and south of Mexico City. When analyzing the last 14 years, the highest moisture values occurred in 2010, 2015 and 2018, while the most severe drought values were recorded in 2017, 2019, 2020, 2021 and 2023.
Face Recognition Based Automated Attendance Management System
Authors: Orozov Bekturali , Isaev Ruslan
Published: January 2025

Attendance is an important aspect of the daily classroom evaluation. When using traditional methods such as calling out roll calls or taking a student's signature, managing attendance can be a time-consuming task. The teacher normally checks it, although it's possible that a teacher will miss someone or some students' answers many times. Face recognition-based attendance system is a solution to the problem of recognizing faces for the purpose of collecting attendance by utilizing face recognition technology based on high-definition monitor video and other information technology. Instead of depending on time-consuming approaches, we present a real-time Face Recognition System for tracking student attendance in class in this work. The suggested method included identifying human faces from a webcam using the Viola-Jones technique, resizing the identified face to the desired size, and then processing the resized face using a basic Local Binary Patterns Histogram algorithm. After the recognition is completed, the attendance will be immediately updated in a database with the relevant information. Many institutions will profit greatly from this endeavor. As a result, the amount of time it takes and the number of human errors it makes are minimized, making it more efficient.
Simultaneous Determination of Six Acidic Herbicides and Metabolites in Plant Origin Matrices by QuEChERS-HPLC-MS/MS
Authors: Jin Qiqi , Xu Qianwen , Zhao Zhiyong , Si Wenshuai , Bai Bing , Chen Lei , Zhou Changyan
Published: January 2025

This study presents a method for the simultaneous determination of six acidic herbicides and their metabolites in various matrices, including fruits, vegetables, grains and edible oils. The method employs acidified acetonitrile extraction combined with dispersive solid-phase extraction cleanup(dSPE) using magnesium sulfatem, Florisil, and graphitized carbon black (GCB). The analysis was performed using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Acetonitrile and 2 mmol.L-1 ammonium formate aqueous solution with a volume ratio of 0.1%(v/v) formic acid were used as mobile phases, target pesticides were analyzed by HPLC-MS/MS in both positive and negative at an ESI ion source under multiple reaction monitoring(MRM). The mass concentrations of six herbicide pesticides and their metabolites showed good linearity with the corresponding peak area in the range of 0.5~50 μg/L, and the correlation coefficient was more than 0.993. The limits of method detection（LODs）ranged from 0.0001 to 0.008 mg/kg. The recovery rates of adding recovery experiments to cabbage, chives, pear, wheat flour and soybean oil were 69.8%~120%, and the relative standard deviation (RSD) was 0.6%~19.5%. The results indicate that this method is efficient and fast, and can be used for the detection of actual samples in various matrices.
Working Conditions and Well-being of School Nurses in Spain: Impact on Job Satisfaction and Professional Quality of Life
Authors: Zafra-Agea José Antonio , Ramírez-Baraldes Estel·la , Maldonado-Manzano Eduard , Obradors-Rial Núria , Puiggrós-Binefa Antonia , Colillas-Malet Ester
Published: January 2025

School nurses play a crucial role in promoting student health, addressing issues such as substance use, mental health, physical health, violence, and sexual health. However, their job satisfaction has been understudied, particularly in relation to the challenges they face. This study evaluates job satisfaction and professional quality of life among school nurses and nurses working in educational settings in Spain. Methods: A descriptive cross-sectional study was conducted (November 2023 - February 2024) with 553 nurses from various regions of Spain, using the Font-Roja Job Satisfaction and CPV-35 Professional Quality of Life questionnaires. Results showed significant differences in job satisfaction between nurses in healthcare and educational settings, influenced by factors such as workload and institutional support. Most participants (97.5% women, median age ~40 years) had less than 5 years of experience. The lack of a dedicated nurse in half of the schools highlights disparities in service provision. Discussion: This study emphasizes the need for supportive policies and work environments to improve the well-being and job satisfaction of nurses in school settings.
The Future of Education: A Multi-Layered Metaverse Classroom Model for Immersive and Inclusive Learning
Authors: Yeganeh Leyli Nouraei , Fenty Nicole Scarlett , Chen Yu , Simpson Amber , Hatami Mohsen
Published: January 2025

This paper envisions an innovative framework to tackle challenges in modern education through a comprehensive Metaverse classroom model. Drawing on immersive learning theories and filling critical gaps in current virtual learning approaches, the study structured a Metaverse-driven Multi-layered Immersive Learning Environment (Meta-MILE). The Meta-MILE framework integrates essential elements such as Metaverse infrastructure, personalized interactions, social collaboration, and advanced assessment techniques. The Meta-MILE model emphasized immersive Three-Dimensional (3D) environments, Artificial Intelligence (AI)-driven personalization, gamified learning pathways, and scenario-based evaluations to boost engagement and provide customized learning experiences. In light of potential challenges, we explored issues around accessibility, infrastructure demands, and data security, suggesting practical strategies to ensure equitable access and secure interactions. Empirical findings underscored Meta-MILE’s effectiveness in enhancing engagement and skill acquisition, and the discussion addressed educational policy implications and the role of immersive classrooms in moving toward competency-based, experiential learning. Future research recommendations included longitudinal studies to validate learning outcomes and continued technological innovation to make virtual learning more accessible and secure. Ultimately, this study highlighted the transformative potential of the Metaverse classroom in creating inclusive, future-ready educational environments tailored to 21st-century needs.
Evaluation of Grading and Estrogen Receptors in Breast Cancer Using a Fully Automated Rapid Immunohistochemistry Based on Alternating Current Electric Field Technology
Authors: Kudo Chiaki , Terata Kaori , Nanjo Hiroshi , Nomura Kyoko , Yuko Hiroshima , Takahashi Eriko , Yamaguchi Ayuko , Konno Hikari , Onji Masaaki , Wakamatsu Yuki , Kimura Yoshihiko , Tskashima Shinogu , Wakita Akiyuki , Yusuke Sato , Minamiya Yoshihiro , Imai Kazuhiro
Published: January 2025

Background: Immunohistochemistry (IHC) is crucial for determining cancer treatments. We previously developed a rapid IHC method and have now developed a fully automated rapid IHC stainer (R-Auto). The aim of this study was to evaluate the clinical reliability of the R-Auto protocol for staining estrogen receptors (ERs) in breast cancer specimens and evaluated the staining performance. Methods: Between January 2015 and June 2020, 188 surgical specimens collected from breast cancer patients treated at our hospital were evaluated through ER staining using R-Auto, conventional manual (conventional) IHC and a commercial autostainer. The specimens were scored using Allred scores, after which the staining results were compared between R-Auto and the conventional IHC or commercial autostainer. Weighted kappa coefficients and AC1 statistics were used to assess the agreement between methods. Results: The AC1 statistic for comparison between R-Auto and conventional IHC was 0.9490 (0.9139-0.9841) with a 95.7% agreement rate and was 0.9095 (0.8620-0.9570) with a 92.6% agreement rate for comparison between R-Auto and the commercial autostainer. There was thus substantial agreement between R-Auto and both conventional IHC and the commercial autostainer. However, R-Auto shortened the time required for IHC from 209 minutes with conventional IHC to 121 minutes. Conclusions: R-Auto enables good staining performance in a shorter time with less effort.
Investigating the Association Between Central Sensitization and Breathing Pattern Disorders: A STROBE-Compliant Cross-Sectional Study
Authors: Lim Hyunmo , Lee Yongwook , Cha Yechan , Hwang Juhee , Han Hyojung , Lee Huijin , Yang Jaeho , Jeong Woobin , Lim Yujin , Lee Donggeun , Kim Hyunjoong
Published: January 2025

Background/Objectives: Central sensitization (CS) is identified as a cause of pain in various musculoskeletal diseases, and breathing pattern disorders (BPDs) are reported to be correlated with chronic pain. This study aimed to analyze the correlation between CS and BPDs. Methods: A cross-sectional study was designed according to the strengthening the reporting of observational studies in epidemiology (STROBE) guidelines. Forty participants with moderate to extreme CS (central sensitization inventory for Koreans; CSI-K≥40) were enrolled, and their respiratory motion (manual assessment of respiratory motion; MARM), respiratory function (self evaluation of breathing questionnaire; SEBQ), respiratory muscle strength (maximal inspiratory pressure; MIP, maximal expiratory pressure; MEP), pain intensity (numeric pain rating scale; NPRS), pain cognition (Korean version of pain catastrophizing scale; K-PCS), muscle tone and stiffness were measured. Results: Among participants with moderate to extreme CS, 82.5% showed BPDs and 42.5% reported severe pain intensity. K-PCS demonstrated significant correlations with MARM area (r=-0.437) and SEBQ (r=0.528). In the subgroup with BPDs, strong correlations were found between MARM area and NPRS (r=-0.486) and K-PCS (r=-0.605). The regression model for muscle properties showed notably high explanatory power (97.8%). Conclusion: In individuals with moderate to extreme CS, respiratory dysfunction was prevalent and significantly associated with pain intensity and pain cognition. These quantitative relationships between breathing mechanics, pain measures, and muscle properties suggest the importance of assessing breathing patterns in CS management.
When frass meets gold: development of a TaqMan Probe-Based qPCR for identifying goldspotted oak borer, Agrilus auroguttatus Schäffer
Authors: Rizzo Domenico , Carli Marco , Zubieta Claudia Gabriela , Marrucci Andrea , Ranaldi Chiara , Palmigiano Bruno , Bartolini Linda , Moriconi Michela , Nugnes Francesco
Published: January 2025

This study presents a qPCR-based molecular diagnostic method utilizing a TaqMan probe to ac-curately identify Agrilus auroguttatus Schäffer (Coleoptera: Buprestidae) from adult insects, ex-creta samples, and frass residues. The method demonstrated excellent DNA amplification across all sample types, highlighting strong analytical specificity. The protocol exhibited high repeata-bility and reproducibility, with inter-run and intra-run variability standard deviations consist-ently at or below 0.5. This assay distinguished A. auroguttatus samples from non-target species with 100% sequence correspondence. Analytical sensitivity (LoD) for adult and frass samples was determined at 8 pg/µL and at a 1:5 dilution from an initial concentration of 5 ng/µL, yield-ing mean Cq values of 35.14 ± 0.71 and 37.07 ± 0.59, respectively. The indirect diagnostic capabil-ity of this method is particularly valuable for detecting the presence of wood-boring insects, po-tentially aiding in the early detection of A. auroguttatus in new environments or in managing cur-rent outbreak areas. This diagnostic tool could thus play a critical role in biosecurity efforts to limit the spread of this invasive species.
Survey on Hybrid Cybersecurity Approaches: Machine Learning, Fuzzy Systems, and Cryptographic Techniques
Authors: Al Khaldy Mohammad
Published: January 2025

Cyber threats continue to escalate in both sophistication and scale, jeopardizing individuals, organizations, and critical infrastructure worldwide. Traditional cybersecurity measures based on static rule sets and signature-based detection often prove inadequate against zero-day exploits, polymorphic malware, and advanced persistent threats. To address these shortcomings, researchers and practitioners are increasingly turning to hybrid cybersecurity frameworks that incorporate machine learning, fuzzy logic, and cryptographic techniques. Machine learning offers dynamic and data-driven threat detection; fuzzy logic accommodates uncertainty and imprecision; while cryptography protects data confidentiality, integrity, and availability. By synergizing these three domains, a multi-faceted security approach emerges—one that can detect novel attacks, adapt to evolving threat landscapes, and secure resources even in distributed resource-constrained environments. This survey explores the theoretical foundations of these hybrid methodologies, reviews cutting-edge applications in phishing detection and IoT security, and discusses advancements in quantum-resistant cryptography. The survey concludes by examining current challenges—including scalability, adversarial robustness, and explainability—and proposing future directions to guide the development of next-generation cybersecurity ecosystems.
Occupational Health Nursing As A Compulsory Subject: Innovation In A Spanish University Degree
Authors: Ranchal-Sanchez Antonio , Montiel Ruiz Elena , Crespo Montero Rodolfo , Romero Saldaña Manuel
Published: January 2025

Background/Objectives: The objective of this manuscript was to explore the situation of Occupational Health Nursing as a mandatory subject in the curriculum of Nursing Degrees in Spanish Universities and compare it with the situation in the Medicine ones. Methods: For this purpose, a integrative review study was carried out including both scientific and gray literature. Results: The results show that, in Nursing Degrees only two universities offered, since the 2024-2025 academic year, compulsory subjects, one of which offers it with its own corpus and 3 credits, while in Medicine only one university offered it as mandatory (along with legal). Conclusions: The innovative proposal on the incorporation of a mandatory and specific subject about occupational health, along with others associated with nursing specialties, could serve as a model for its incorporation into Spanish university Nursing Degrees.
Ulva Seaweed-Derived Ulvan: A Promising Marine Polysaccharide for Biomaterial Design
Authors: Pari Rizfi Fariz , Uju Uju , Hardiningtyas Safrina Dyah , Ramadhan Wahyu , Wakabayashi Rie , Goto Masahiro , Kamiya Noriho
Published: January 2025

Currently, green seaweed is underutilized compared to other major seaweeds. Among green seaweeds, many scientists have reported the utilization of Ulva in various fields in recent years, which intrigues its potential as the next top candidate for biomass in industrial biorefineries. Ulva has unique polysaccharides called ulvan, which are considered for medicinal and pharmacological applications. Ulvan is a sulfated polysaccharide consisting of rhamnose, glucuronic acid, and sulfated sugar units, which offers a range of bioactive properties, including immunomodulation, antimicrobial activity, anticoagulant, and biocompatibility, making it a versatile candidate for biomaterial design. This review presents an in-depth analysis of the potential route for the application of ulvan, starting from extraction methods, structural and biological characterization to biomaterial design, highlighting its advantages over traditional polysaccharides such as agar, carrageenan, and alginate.
Prime Pair Products Based on Prime Gaps
Authors: Gocgen Ahmet F. , Sahin Berke , Buyukyayla Emin M.
Published: January 2025

This paper investigates the arithmetic structure of prime pair products by analyzing the gaps between primes. Focusing on pairs, we explore patterns in the distribution of their products. We establish that prime pairs with gaps a power of 6 yield products in the form 6n+1, while those without power of 6 gaps result in products of the form 6n+5, except where 2 and 3 are found, offering a foundational understanding of their algebraic properties.
Teak Triumph: ModelIing Farmer Decisions for Carbon Market Participation
Authors: Sackey Emmanuel Kwaku , Kwakwa Paul Adjei , Ansah Mercy Owusu , Derkyi Mercy Afua Adutwumwaa
Published: January 2025

Teak (Tectona grandis), a globally valued hardwood, is transforming sustainable forestry and carbon credit markets. This study examines drivers of Teak plantation adoption in Ghana's Forest-Savannah Transition Landscape, focusing on reforestation and carbon credit generation. Using the Theory of Planned Behaviour and Partial Least Squares Path Modelling, we analyse economic, social, and practical factors influencing farmer decisions. Findings reveal that Teak's promise of stable income and profitability is a key motivator, making positive economic perceptions critical. Social influences, including government incentives and community pressures, also encourage adoption but are tempered by cultural barriers that hinder implementation. Limited perceived behavioural control, caused by resource constraints and inadequate support, negatively impacts intentions, emphasizing the need to address practical challenges. The interplay between attitudes, subjective norms, and perceived behavioural control shapes Teak's acceptance as a carbon credit source. Closing resource gaps, strengthening community engagement, and framing Teak as both profitable and climate-friendly is essential for overcoming barriers and unlocking its potential. This study calls for innovative policies, targeted incentives, and grassroots engagement to position Teak plantations as a cornerstone of economic resilience and environmental restoration. With these insights, Ghana can take charge of aligning local practices with global climate ambitions.
In Silico Identification of Banana High-Confidence MicroRNA Binding Sites Targeting Banana Streak GF Virus
Authors: Ashraf Muhammad Aleem , Ali Babar , Fareed Maryam , Sardar Ahsan , Saeed Eisha , Islam Samaa , Bano Shaher , Yu Naitong
Published: January 2025

Banana streak GF virus (BSGFV) is the extremely dangerous monopartite badnavirus (genus, Badnavirus; family, Caulimoviridae) of banana (Musa acuminata AAA Group) that imposes a serious threat to global banana production. The BSGFV causes a devastating pandemic in banana crops, transmitted by deadly insect pest mealybug vectors and replicate through an RNA intermediate. The BSGFV is a reverse-transcribing DNA virus that has a monopartite open circular double-stranded DNA (dsDNA) genome with a length of 7325 bp. RNA interference (RNAi) is a natural mechanism that has revolutionized target gene regulation of various organisms to combat virus infection. The current study aims to locate the potential target binding sites of banana-encoded microRNAs (mac-miRNAs) on the BSGFV dsDNA-encoded mRNAs, based on three algorithms, RNA22, RNAhybrid and TAPIR. Mature banana (2n = 3x = 33) miRNAs (n = 32) were selected and hybridized to the BSGFV genome (MN296502). Among the 32 targeted mature locus-derived mac-miRNAs investigated, two banana mac-miRNA homologs (mac-miR162a and mac-miR172b) were identified as promising naturally occurring biomolecules, to have binding affinity at nucleotide positions 5502 and 9 of the BSGFV genome. The in silico banana genome-encoded mac-miRNA/mbg-miRNA-regulatory network was developed with the BSGFV— ORFs using Circos software to identify potential therapeutic target proteins. Therefore, the current work provides useful biological material and opens a new range of opportunities for generating BSGFV-resistant banana plants through genetic manipulation of the selected miRNAs.
Hypothalamic Hamartomas: A State-of-the-Art Review
Authors: Mitrică Marian , Manole Aida-Mihaela , Toma Mihai , Sirbu Octavian Mihai , Sirbu Anca Maria , Munteanu Alice Elena
Published: January 2025

Hypothalamic hamartomas (HHs) are rare, non-neoplastic malformations of the hypothalamus with heterogeneous clinical presentations, including gelastic seizure, central precocious puberty, and cognitive or behavioural impairments. This narrative review synthesizes current knowledge regarding the etiology, clinical manifestations, diagnostic advances, and therapeutic approaches for HH. Genetic insights highlight the role of postzygotic mosaicism and dysregulated Sonic Hedgehog signalling in HH development, emphasizing their relevance in potential therapeutic strategies. Diagnostic modalities such as MRI, PET, and SEEG play pivotal roles in identifying and characterizing HHs, enabling precise treatment planning. Therapeutic interventions span pharmacological, surgical, and neuromodulatory approaches. While surgical techniques such as transcallosal resection or stereotactic radiosurgery offer substantial seizure control, newer modalities like laser interstitial thermal therapy (LITT) and stereotactic radiofrequency thermocoagulation prioritize minimizing cognitive and behavioural sequelae. Pharmacological management and neuromodulation offer adjunctive benefits, particularly in drug-resistant epilepsy despite advancements, limitations persist, including outcome variability and insufficient long-term studies. This review underscores the need for multidisciplinary care and advanced research to optimize outcomes and improve the quality of life for patients with HH.
The Role of Oxidative Stress in Autism Spectrum Disorder Pathophysiology, Diagnosis and Treatment
Authors: Kuźniar-Pałka Aleksandra
Published: January 2025

Autism spectrum disorder is an important health problem with no known single cause. There is a vast number of evidence showing the important role of oxidative stress in that disorder. The article's authors made the current literature research to summarize knowledge on that topic. In this paper the role of oxidative stress was studied in the context of its influence on pathogenesis, using oxidative stress biomarkers as diagnostic tools and use of antioxidants in ASD treatment. There is growing data on the involvement of oxidative stress in ASD pathogenesis as well as its direct effect and also by its interlay with inflammation and mitochondrial dysfunction. Oxidative stress biomarkers seem to have good potential to be used as diagnostic tools supporting early ASD diagnosis. Antioxidants show good potential in ASD-supportive treatment. In all described fields data support the importance of oxidative stress but also a need for further research.
β2-Chimaerin Deficiency Favors Polyp Growth in the Colon of ApcMin/+ Mice
Authors: Velasco-Sampedro Eladio A. , Sanchez-Vicente Cristina , Caloca María J.
Published: January 2025

A Rho-GTPases are pivotal regulators of key cellular processes implicated in colorectal cancer (CRC) progression, yet the roles of their regulatory proteins, particularly GTPase-activating proteins (GAPs), remain poorly understood. This study focuses on β2-chimaerin, a Rac1-specific GAP, in Apc-driven tumorigenesis using the ApcMin/+ mouse model. We demonstrate that β2-chimaerin deficiency selectively promotes the growth of colonic polyps without influencing small intestinal polyp formation. Mechanistically, β2-chimaerin loss is associated with enhanced ERK phosphorylation, while canonical Wnt/β-catenin and E-cadherin pathways remain unaffected, suggesting its specific involvement in modulating proliferative signaling in the colon. Consistent with its tumor-suppressive role, bioinformatics analyses reveal that low β2-chimaerin expression correlates with poor prognosis in CRC patients. This study expands the understanding of Rho-GTPase regulatory mechanisms in intestinal tumorigenesis, providing a basis for future therapeutic strategies targeting Rho-GTPase pathways in CRC.
Arduino-Powered Smart System for Real-Time Monitoring of Northern Indias Drinking Water Quality
Authors: Singh Arjun , Arya Rahul , Choudhary Nikki , Poudel Navraj , Arora Poonam , Maurya Kamlesh K. , Mandal Tuhin Kumar , SHARMA SUDHIR KUMAR , Kumar Prashant , Pant R.P. , Pathak Saurabh
Published: January 2025

Continuous monitoring of drinking water quality is essential for safeguarding public health, particularly in densely populated urban areas like the National Capital Region (NCR) of Delhi. In this study, 47 water samples were randomly collected from various public sources across Delhi and its surrounding areas using a standardized sampling technique. An Arduino-based sensor system was employed to measure critical water quality parameters, including pH, total dissolved solids (TDS), and turbidity. While pH levels were consistently within the acceptable limits across all samples, approximately 14% of the samples exhibited elevated TDS and turbidity levels, raising concerns about potential contamination. In contrast, commercially packaged drinking water samples were found to have significantly lower TDS and turbidity levels, suggesting a higher level of quality control in packaged water compared to public sources. These findings emphasize the importance of continuous water quality monitoring to identify and mitigate potential health risks in real time. This study demonstrates the feasibility and effectiveness of Arduino-based systems as a cost-effective, scalable solution for real-time water quality assessment in urban environments. By leveraging sensor technology, this approach offers a practical means of ensuring safe drinking water in Delhi and similar densely populated regions.
Automated Research Review Support using Machine Learning, Large Language Models, and Natural Language Processing
Authors: Pendyala Vishnu S. , Kamdar Karnavee , Mulchandani Kapil
Published: January 2025

Research expands the boundaries of a subject, economy, and civilization. Peer review is at the heart of research and is understandably an expensive process. This work, with human-in-the-loop, aims to support the research community in multiple ways. It predicts quality, and acceptance, and recommends reviewers. It helps the authors and editors to evaluate research work using machine learning models developed based on a dataset comprising of 18,000+ research papers, some of which are from highly acclaimed, top conferences in Artificial Intelligence such as NeurIPS and ICLR, their reviews, aspect scores, and accept/reject decisions. Using Machine Learning algorithms such as Support Vector Machines, Deep Learning Recurrent Neural Network architectures such as LSTM, a wide variety of pre-trained word vectors using Word2Vec, GloVe, FastText, transformer architecture based BERT, DistilBERT, Google’s Large Language Model (LLM), PaLM 2, and TF-IDF vectorizer, a comprehensive system is built. For the system to be readily usable and to facilitate future enhancements, a frontend, a flask server in the cloud, and a NOSQL database at the backend were implemented making it a complete system. The work is novel in using a unique blend of tools and techniques to address most aspects of building a system to support the peer-review process. The experiments resulted in a 86% test accuracy on acceptance prediction using DistilBERT. Results from other models were comparable, with PaLM based LLM embeddings achieving 84% accuracy.
Tectono-Stratigraphic Framework and Hydrocarbon Potential in the Albert Rift, Uganda: Insights from Basin and Petroleum System Modeling
Authors: Twinomujuni Lauben , Liu Keyu , Hassan Hafiz Ahmed Raza , Jia Kun , Wang Shunyu , Sserubiri Tonny , Summer Mathias
Published: January 2025

The Albert Rift in Uganda is a significant geological and petroleum exploration frontier within the East African Rift System. The basin has been comprehensively analyzed via thorough literature survey, seismic data analysis, well log interpretations, and basin and petroleum systems modeling to examine the complex interactions of tectonics, sedimentation, and hydrocarbon generation and expulsion within the rift basin. Our findings reveal a detailed tectonostratigraphic framework with multiple Neogene to Quaternary depositional sequences and structural features influencing hydrocarbon maturation, generation and expulsion. Key stratigraphic units are identified, highlighting their contributions to a viable petroleum system present within the rift basin. The Albert Rift is a Neogene petroleum system that is currently generating and expelling hydrocarbons to various potential traps. The study underscores the impact of rift-related tectonics on the basin architecture, source-reservoir-seal rock distribution within a young rift basin. Our research enhances new understanding of the Albert Rift's geological history and petroleum systems, offering valuable insights for future petroleum exploration strategies. By integrating tectonostratigraphic and petroleum system analysis, we provide a robust model applicable to similar rift basins worldwide.
Inflammation and Fibrosis at Pancreatic Resection Margin and Their Role In Post-Operative Pancreatic Fistula Development After Pancreaticoduodenectomy: A Pilot Study From A Single Institution
Authors: La Vaccara Vincenzo , Cammarata Roberto , Costa Gianluca , Taffon Chiara , Olivieri Laura , Farolfi Tommaso , Angeletti Silvia , Giordano Federica , Pretini Chiara , Coppola Roberto , Caputo Damiano
Published: January 2025

Background/Objectives: The absence of pancreatic fibrosis is a known risk factor for post-operative pancreatic fistula (POPF) following pancreaticoduodenectomy (PD). Emerging evidence suggests inflammation plays a role in anastomotic healing. This study aimed to assess the role of chronic lymphomononuclear inflammatory infiltrate (CLII) and fibrosis at the pancreatic resection margin (PRM) on POPF development and explored a method for its intraoperative prediction. Materials and Methods: Data from 141 patients who underwent PD between 2014 and 2022 were retrospectively analyzed. PRM were histologically evaluated for fibrosis and CLII. Univariate and multivariate analyses were performed to identify potential predictors of postoperative pancreatic fistula (POPF) and clinically relevant postoperative pancreatic fistula (CR-POPF). Results: The histopathological analysis of intraoperative frozen sections of the PRM showed a strong association between the absence of fibrosis and CLII and increased risk of POPF and CR-POPF (OR 7.51, p < 0.0001; OR 4.30, p < 0.0001). Multivariate analysis further identified a main pancreatic duct diameter of less than 3 mm, as well as the combined presence of fibrosis and inflammation, as independent risk factors for developing POPF (OR 4.22, p = 0.001) and CR-POPF (OR 4.83, p = 0.020). Conclusion: The absence of fibrosis and CLII at the PRM significantly increases the risk of POPF and CR-POPF. This study validated a reproducible method for intraoperative assessment of fibrosis and CLII, enabling the identification of high-risk cases. Such a tool could guide surgical strategies to mitigate POPF-related complications, improving patient outcomes after PD.
Investigating Past, Present, and Future Trends on the Interface Between Marine and Medical Research & Development: A Quantitative Strategic Foresight Analysis
Authors: Zamani Mehdi , Melnychuk Tetyana , Eisenhauer Anton , Gäbler Ralph , Schultz Carsten
Published: January 2025

The convergence of marine sciences and medical studies has the potential for substantial advances in healthcare. This study uses bibliometric and topic modeling studies to map the progression of research themes from 2000 to 2023, with an emphasis on the interdisciplinary subject of marine and medical sciences. Building on the global publication output at the interface between marine and medical sciences and using the Hierarchical Dirichlet Process, we discovered dominating research topics during three periods, emphasizing shifts in research focus and development trends. Our data show a significant rise in publication output, indicating a growing interest in using marine bioresources for medical applications. The paper identifies two main areas of active research: "natural product biochemistry" and "trace substance and genetics", both with great therapeutic potential. We used social network analysis to map the collaborative networks and identify the prominent scholars and institutions driving this research & development progress. Our study indicates important paths for research policy and R&D management operating at the crossroads of healthcare innovation and marine sciences. It also underscores the significance of quantitative foresight methods and inter-disciplinary teams to identify and interpret future scientific convergences and breakthroughs.
LncRNAs in Ovarian Cancer: Mechanistic Insights and Clinical Applications
Authors: Basu Sneha , Nadhan Revathy , Dhanasekaran Danny N.
Published: January 2025

Background/Objectives: Ovarian cancer is a leading cause of gynecological cancer mortality worldwide, often diagnosed at advanced stages due to vague symptoms and lack of effective early detection methods. Long non-coding RNAs (lncRNAs) have emerged as key regulators in cancer biology, influencing cellular processes such as proliferation, apoptosis, and chemoresistance. This review explores the multifaceted roles of lncRNAs in ovarian cancer pathogenesis and their potential as biomarkers and therapeutic targets. Methods: A comprehensive literature review was conducted to analyze the structural and functional characteristics of lncRNAs and their contributions to ovarian cancer biology. This includes their regulatory mechanisms, interactions with signaling pathways, and implications for therapeutic resistance. Advanced bioinformatics and omics approaches were also evaluated for their potential in lncRNA research. Results: The review highlights the dual role of lncRNAs as oncogenes and tumor sup-pressors, modulating processes such as cell proliferation, invasion, and angiogenesis. Specific lncRNAs, such as HOTAIR and GAS5, demonstrate significant potential as di-agnostic biomarkers and therapeutic targets. Emerging technologies, such as single-cell sequencing, provide valuable insights into the tumor microenvironment and the het-erogeneity of lncRNA expression. Conclusions: LncRNAs hold transformative potential in advancing ovarian cancer di-agnosis, prognosis, and treatment. Targeting lncRNAs or their associated pathways offers promising strategies to overcome therapy resistance and enhance personalized medicine. Continued research integrating omics and bioinformatics will be essential to unlock the full clinical potential of lncRNAs in ovarian cancer management.
Development of a MetUM (v 11.1) and NEMO (v 3.6) Coupled Operational Forecast Model for the Maritime Continent: Evaluation of Atmospheric Forecasts
Authors: Kumar Rajesh , Sánchez Claudio , Heng Peter , Thompson Bijoy , Huang Xiang-Yu , Ng Zhi Hao Alvin , Furtado Kalli , Zhang Hugh
Published: January 2025

The Maritime Continent (MC), located between the Pacific and Indian Oceans, is a critical region for global weather and climate systems due to its complex geography, intense precipitation, and vital role of air-sea interactions. Accurate modeling of coupled ocean-atmosphere dynamics in this region can potentially improve weather forecasts and climate projections. This study presents the development and evaluation of a fully coupled operational forecast model, integrating the UK Met Office atmospheric model (Unified Model v11.1) with the Nucleus of European Modelling of the Ocean (NEMO v3.6), at a horizontal resolution of 4.5 km, specifically tailored for the MC. The coupled model is designed to enhance atmospheric forecast accuracy by capturing air-sea interactions. Model performance was assessed using observational datasets and reanalysis products, focusing on precipitation, wind speed, and air temperature. Results show an improved forecast accuracy for the coupled model over a standalone atmospheric model. A case study on Typhoon Nakri further demonstrates an improved 10 m wind speed simulations with the coupled model, aligning more closely with reanalysis data. Air temperature forecasts, especially during nighttime and at longer lead times, also benefited from ocean-atmosphere coupling, with verification against meteorological stations confirming improved accuracy in regions with strong oceanic influence.
Unveiling the Resistome Landscape in Peri-Implant Health and Disease
Authors: Bessa Lucinda J. , Egas Conceição , Botelho João , Machado Vanessa , Alcoforado Gil , Mendes José João , Alves Ricardo
Published: January 2025

Background: The human oral microbiome is a critical reservoir for antibiotic resistance, however, subgingival peri-implant biofilms remain underexplored in this context. We aimed to explore the prevalence and distribution of antibiotic resistance genes (ARGs) in metagenomes derived from saliva and subgingival peri-implant biofilms. Methods: A total of 100 metagenome datasets from 40 individuals were retrieved from the Sequence Read Archive (SRA) database. Of these, 20 individuals had exclusively healthy implants and 20 had both healthy and affected implants with peri-implantitis. ARGs and their taxonomic assignments were identified using the ABRicate tool, and plasmid detection was performed with PlasmidFinder. Results: Four plasmid replicons were identified in 72 metagenomes, and 55 distinct ARGs from 13 antibiotic classes were detected in 89 metagenomes. The repUS43 plasmid replicon and ARGs conferring resistance to macrolides-lincosamides-streptogramins, tetracyclines, beta-lactams, and fluoroquinolones were the most prevalent. The msr(D) and mef(A) genes showed the highest prevalence, except in saliva samples from individuals with healthy implants, where mef(A) ranked fourth. Saliva samples harbored greater ARG diversity than subgingival biofilm samples. Principal Coordinates Analysis of Jaccard distances revealed no significant differences between healthy and peri-implantitis-affected subgingival biofilm groups (pairwise PERMANOVA, p > 0.05). The taxonomic origins of the detected ARGs were also obtained to better understand their distribution and potential impact on oral microbial communities. Conclusions: Resistome profiles associated with both peri-implant health and disease showed no significant differences and higher salivary abundance of ARGs compared to subgingival biofilm samples.
Enhanced Wi-Fi Sensing: Leveraging Phase and Amplitude of CSI for Superior Accuracy
Authors: Quy Thai Duy , Lin Chih-Yang , Shih Timothy K.
Published: January 2025

Human activity recognition (HAR) in indoor environments using Wi-Fi channel state information (CSI) remains a topic of significant interest and has seen rapid development in recent years. This field offers great potential due to the widespread availability of Wi-Fi signals and their cost-effectiveness, with applications in areas such as elderly care systems, context-aware environments, and security monitoring. However, Wi-Fi-based HAR faces considerable challenges in maintaining consistent performance across various environments and individuals, primarily due to the inherent variability of Wi-Fi signals. Addressing this challenge requires training models on large and diverse datasets to ensure robustness and generalization across different environments and conditions. Furthermore, most existing research focuses on recognizing human gestures by extracting a single feature from Wi-Fi signals over time sequences, while neglecting multi-channel signal information and underutilizing other valuable features. To overcome this limitation, we propose a model named PA-CSI that leverages both amplitude and phase features from Wi-Fi signals, incorporating attention mechanisms across both temporal and channel dimensions, along with multi-scale convolutional neural networks (CNNs). The PA-CSI model demonstrates competitive accuracies across datasets: 99.9% on StanWiFi, 98% on MultiEnv, and 99.9% on a self-constructed dataset. The source code is available at https://github.com/thai-duy-quy/PA-CSI-HAR.
Spatial-Temporal Pattern and Stability Analysis of Zooplankton Community Structure in the Lower Yellow River, China
Authors: Wang Yaowei , Zhang Shiyuan , Sun Minfang , Han Jiamin , Wang Ziyue , Chen Xinlei , Chen Zengfei , Qin Haiming
Published: January 2025

In March (spring), June (summer), October (autumn) and December (winter) 2022, zooplankton had been quantitatively investigated in the lower reaches of the Yellow River in China. A total of 29 sampling points which were separated by about 20 km were set up in the survey reach. The purpose of this study is to investigate the seasonal dynamic changes and spatial distribution characteristics of zooplankton community in the Yellow River with high sediment content. The main results are as follows: A total of 119 species of zooplankton were found during the survey, including 70 species of Rotifers, 29 species of Cladocerans and 20 species of Copepods. Because the temperate continental monsoon climate had four distinct seasons, the zooplankton community in the Yellow River showed typical seasonal dynamic changes. There were significant differences in the richness of zooplankton and dominant species in four seasons (P<0.05). There were 15 coexisting species in four seasons, among which Brachionus calyciflorus was the dominant species. The density and biomass of zooplankton were significantly higher in spring and summer than in autumn and winter (P<0.05). The results of multidimensional non-metric ranking (NMDS) based on zooplankton abundance showed four distinct communities, which were spring community, summer community, autumn community and winter community. Affected by human activities (water and sediment regulation, urban sewage input) and natural factors (river and lake water input, estuarine tides), the zooplankton community presented a typical spatial heterogeneity. The density and biomass of zooplankton were significantly lower in estuarine reach than in other reaches. The spatial heterogeneity of zooplankton communities in spring, summer and autumn was significantly different (P<0.05). However, only the estuarine reach had a special zooplankton community in winter. Monte Carlo test results showed that pH, water temperature, electrical conductivity, dissolved oxygen, total nitrogen and total phosphorus were the main water environmental factors affecting the community structure of zooplankton (P<0.05). The reaches of the Yellow River affected by human disturbances have lower zooplankton community stability. Overall, the standing stock of zooplankton was very low (less than 15 ind./L) but the species richness was higher (119 species) in the river with high sediment content, fast flow and oligotrophic levels.