bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biometeorological and Molecular Innovative Study for Modelling and Assessing the Impact of Heat Stress on Water Buffalo Dairy Production: Promising Adaptive Strategies to Cope with Climate Change in Egypt
Authors: Ismail Eman M. , Aly Aly M. , Farag Heba S. , Kamel Shaimaa , Fahim Karima M. 
Published: January 2025

Abstract

Background Growing global warming significantly challenges buffalo dairy production, particularly in subtropical climates where summer heat exceeds their thermal comfort zone. Since 2014, Egypt's buffalo milk production has declined. However, few studies have addressed interventions to alleviate the underlying causes. This study proposed a short-term adaptation strategy based on enhancing environment and management practices to alleviate the impact of heat stress (HS) on the physiological and production performance of 12 lactating Egyptian buffaloes (Bubalus bubalis) over 47 summer days in Mit Helfa, Egypt. Meteorological data were monitored daily, and temperature humidity index (THI) was calculated. The daily milk yield (DMY) was recorded alongside the rectal temperature (RT) and respiratory rate (RR). Milk samples were also collected for molecular, biochemical, and quality assessments.
Results Two HS models were defined based on THI data. HS Model I (Group A) represented the critical, severe, and moderate HS zones (CHSZ, SHSZ, and MHSZ). Meanwhile, the HS Model II (modified, Group B) reflects the modulatory effects of adaptive interventions in reducing the THI and remodelling the CHSZ and SHSZ to non-HS zones (NHSZ). Additionally, this study provides the first transcriptional analysis of energy homeostasis genes in water buffaloes. It revealed significantly higher expression levels of AMPK, HRH1, and mTOR genes in the HS-Gp A compared to non-HS Gp B (p>0.05). Production data revealed a significant 53% increase in DMY (13 kg/day) in Gp B with lower oxidative stress and superior milk quality. Correlation and regression statistics indicated that higher THI increased RT and RR and significantly decreased DMY in HS Gp A. Conversely, lower THI in Gp B reduced physiological stress and significantly increased DMY (p>0.001). For each unit increase in THI above 69, the DMY decreases by 0.17 and 0.23 kg/day/buffalo, with total predicted decreases of 1.9 and 2.5 kg when the THI increases from 70 to 83 and 65 to 76 (maximum and minimum THI) in HS and Non-HS groups, respectively.
Conclusion These findings highlight the negative impact of HS on buffalo health and productivity, emphasising the need for adaptive strategies and genetic research to maintain energy balance, support milk production, and develop heat-tolerant breeds.
Metabolites profiling and bioactivity of Aspergillus-Penicilliumco-culture: Integrative GC-MS, LC-MS/MS, and in silico analysis for targeting Fusarium proliferatum in onion basal rot biocontrol
Authors: Abdelrahem Mohammed M. M. , Hassane Abdallah M. A. , Abo-Dahab Nageh F. , Abouelela Mohamed E. 
Published: January 2025

Abstract

Onion basal rot, caused mainly by the soil-borne fungus Fusarium proliferatum, represents a serious and persistent disease problem of onion (Allium cepa L.) worldwide. It results in severe yield reductions, impacting both quantity and quality of crop. This pathogen penetrates roots and results in damage of the basal plate of the onion, leading to decay and wilting. It is important to understand how to appropriately manage this disease, so its adverse effects can be minimized. Here, we examined the metabolites profile and bioactivity of co-culture system of Aspergillus ochraceus and Penicillium chrysogenum against Fusarium proliferatum, one of the main pathogens responsible for onion basal rot. By employing integrative analytical techniques such as GC-MS and LC-MS/MS, the metabolites profile from the co-culture extract was identified, particularly in the dichloromethane fractions. The fraction showed the highest antifungal activity with a minimum inhibitory concentration (MIC) of 0.39 mg/mL and showed the lowest cytotoxicity in brine shrimp assay. A molecular docking study on bioactive chemicals revealed that, among others, spirobrocazine A, palmitic acid, spathullin B, and citridone H were found to make a very high affinity with the cutinase enzyme from F. proliferatum, possibly leading to inhibition mechanisms. The results showed that fungal co-cultures have potential to produce bioactive compounds could be serve in production of biocontrol products with an eco-friendly application to counteract Fusarium infections in plants.
Teacher noticing in inclusive mathematics education: Analyzing its structure and expert-novice differences using a novel video-based test instrument
Authors: König Johannes , Kaiser Gabriele , Bastian Anton , Weyers Jonas , Buchholtz Nils , Ross Natalie 
Published: January 2025

Abstract

Teachers’ professional noticing—often conceptualized as their situation-specific skills of perception, interpretation and decision-making—constitutes an important component of their professional competence. Noticing has become increasingly significant worldwide in the pursuit of inclusive mathematics education in classroom settings, whereby teachers are required to provide equal opportunities for students across all ability levels. However, current teacher noticing frameworks lack the requisite specificity to support inclusive mathematics education, particularly regarding diagnostic applications and adequate learning support, and thus a revision of existing frameworks is warranted. On the one hand, such frameworks must incorporate selected facets of teacher knowledge, including mathematical pedagogical content knowledge (MPCK) and general pedagogical knowledge (GPK) specified for inclusive teaching; on the other hand, the differentiation into dispositions and situation-specific skills as decisive components of competence viewed as a continuum must be considered, as Sigrid Blömeke et al. suggested in their seminal framework. This paper describes the methodological challenges associated with the development of standardized instruments to measure teachers’ professional knowledge and noticing within inclusive mathematics education, as undertaken by the project Teacher Education and Development Study – Inclusive Mathematics Education (TEDS-IME). Using a large sample of 628 pre-service and in-service teachers, the paper aims to examine the dimensionality of teachers’ competence for inclusive mathematics education and expert–novice differences in the pattern of their competence facets. Our findings indicate that the newly developed video-based instrument for teachers’ noticing in inclusive mathematics education serves as a reliable and multidimensional measurement for both pre-service and in-service teachers.
Opportunities to mitigate PM2.5 and nitrogen deposition through agricultural NH3 control strategies in China’s Beijing-Tianjin-Hebei region
Authors: Li Lu , Guo Yixin , Xu Jiayu , Ye Xingpei , Li Danyang , Liu Zehui , Ti  , Liu Xuejun , Zhang Lin 
Published: January 2025

Abstract

High ammonia (NH3) emissions mostly from agricultural sources have contributed to PM2.5 air pollution and excess nitrogen deposition harmful to human and ecosystem health in China. Here we develop an assessment framework which combines an agricultural management technology database, a high-resolution (3 km) agricultural NH3 emission inventory, and the GEOS-Chem High Performance (GCHP) model (at 10 km × 10 km resolution). We evaluate technology combinations for their potentials in providing NH3 emission reductions and consequent air quality and ecosystem benefits for Beijing-Tianjin-Hebei (BTH) region, a hotspot for NH3 and PM2.5. Results show that BTH agricultural NH3 emissions can be reduced by up to 57% (274 Gg N/a) in 2019. With maximum feasible NH3 reduction, annual PM2.5 concentrations and nitrogen deposition can be reduced by up to 7% and 13%, respectively, which are more significant than effects of halving local anthropogenic NOx emissions. When combining maximum NH3 reduction and 50% anthropogenic NOx reduction, effects of NH3 controls on PM2.5 mitigation will be suppressed, while facilitating more efficient local nitrogen deposition mitigation. Our findings implicate that maximum technical feasible NH3 emission reductions are still useful for current PM2.5 management and nitrogen deposition mitigation. In the near future, with continuous NOx controls, additional NH3 controls are required to further mitigate nitrogen deposition in BTH, while delivering air quality benefits that are nonlinearly dependent on the levels NH3 and NOx are reduced.
Structural Behaviour of Functionally Graded High-Strength Hybrid Fiber-Reinforced Concrete Beam-Column Joints Under Static and Cyclic Loading
Authors: P Amsayazhi , RN Karthika 
Published: January 2025

Abstract

The structural behaviour of beam-column connections with varying concrete layers was experimentally evaluated to enhance performance through hybrid fiber-reinforced concrete. A custom hybrid mix incorporating 0.3% glass fiber and 0.7% polypropylene fiber was developed, creating a reinforced layer with high-strength concrete in the beam section. Four beam-column joints were cast, featuring a dual-layer concrete design—hybrid fiber-reinforced concrete and high-strength concrete—and subjected to static loading tests. Comparative analysis with control specimens, devoid of fiber reinforcement, demonstrated a 22.9% improvement in ultimate load-carrying capacity. Crack patterns aligned with the "strong column-weak beam" principle, ensuring optimal failure mechanisms. Probability analysis confirmed the consistency and reliability of the hybrid fiber-reinforced layer in improving structural performance. The results highlighted significant gains in ductility, structural strength, and load-carrying capacity, offering a robust solution to durability and performance challenges in reinforced concrete beam-column connections. This investigation contributes to developing advanced materials for resilient structures, underscoring the benefits of combining hybrid fibers with high-strength concrete.
Morpho-Anatomical Adaptations of Schoenoplectus lacustris (L.) Palla Along the Salinity Gradients in Sargodha Pakistan
Authors: Hassan Muhammad 
Published: January 2025

Abstract

Salinity is a critical environmental stressor that significantly impairs plant productivity, rendering approximately 800 million hectares of agricultural land globally unfit for cultivation. This study investigates the morpho-anatomical adaptations of Schoenoplectus lacustris(L.) Palla, a hyper accumulator sedge from the Cyperaceae family, which dominates the saline wetlands of the Sargodha region. This research represents a novel approach, as such an investigation had not been previously conducted for this species. Plant samples of Schoenoplectus lacustris were collected from natural habitats exhibiting varying levels of salinity. The corresponding soil samples were categorized into highly saline, moderately saline, and less saline based on their physicochemical properties. The species demonstrated optimal growth under highly saline conditions, underscoring its potential as a highly efficient halophyte. Morphological parameters, including plant height, shoot and root biomass (fresh and dry weight), root length, leaf length, and leaf area, were found to be highest in highly saline soils and lowest in less saline conditions. Anatomical adaptations of Schoenoplectus lacustristo salinity stress included reductions in stem cross-sectional area and modifications in root structure. These included increased endodermis thickness, broader xylem vessels, larger parenchymatous cells, and enhanced aerenchyma development. In addition, Schoenoplectus lacustris exhibited complementary responses such as an increase in epidermal cell area, reduced stomatal density, intensified sclerification, enlarged vascular bundle, metaxylem, phloem, and bulliform cell areas. These morpho-anatomical modifications enable Schoenoplectus lacustris to thrive under hypersaline conditions, making it a promising candidate for the rehabilitation of saline soils in Pakistan for both food and fodder production
Cinnamomum tamala (Buch.-Ham.) Th. G. G. Nees Leaves Extract Protects against an Experimental Murine Model of Diabetic Cardiomyopathy
Authors: Kaur Amritpal , Singh Thakur Gurjeet , Khan Nadeem , Gir Hardeep , Arora Poonam , Kumar Manish 
Published: January 2025

Background:
Diabetic cardiomyopathy (DCM) is a critical complication involving
oxidative stress and inflammation, that is not amenable to current therapeutic strategies. Herbal
remedies are increasingly being used as an alternative to current treatment options.

Objectives:
In this research, we evaluated the effects of Cinnamomum tamala (Buch. -Ham.) Th.
G. G. Nees leaves the extract in DCM and its scope as an adjunct or alternative therapy along
with existing standard treatment.

Methods:
The ethanolic extract was prepared using the Soxhlet apparatus and rotary evaporator.
Streptozotocin (1st day) and isoproterenol (12th and 13th day) were given to induce DCM in rats.
Oral treatment with ethanolic extract of C. tamala leaves (250 and 500 mg/kg) or glibenclamide
(10 mg/kg) was given from the 3rd to the 14th day.

Results:
C. tamala extract or glibenclamide attenuated streptozotocin-induced hyperglycemia in
rats. C. tamala extract or glibenclamide (10 mg/kg) inhibited serum dyslipidemia, lactate dehydrogenase,
and creatine kinase MB activities. The body weight, total heart protein, and heart
weight were ameliorated by C. tamala extract or glibenclamide (10 mg/kg). Furthermore, DCM
culminated in cardiac oxidative stress and inflammation, and these biochemical parameters were
ameliorated by C. tamala extract or glibenclamide (10 mg/kg) treatment. The combination of C.
tamala extract (500 mg/kg) and glibenclamide (5 mg/kg, low dose) significantly attenuated DCM
relative to separate treatments.

Conclusion:
C. tamala can be a useful alternative or adjunct therapy in DCM therapeutics. The
combinatorial treatment approach with C. tamala might reduce the dose of therapeutic molecules
used in the current clinical practice of DCM.
Ramadan Fasting and Migraine: Exploring their Association with Circadian Rhythms -A Narrative Review
Authors: Al Rashid Sulthan , Kohaf Neveen A. , Harisa Gamaleldin I. , Ziaur Rahman Syed , AlSheikh Mona Hmoud 
Published: January 2025

Abstract:
Ramadan fasting, practiced by Muslims around the globe, involves changes in eating
and sleeping patterns that can affect circadian rhythms. This review explores the link between circadian
rhythms and migraines during Ramadan fasting. Disruptions in circadian rhythms, which
govern various bodily processes, have been associated with multiple health issues, including migraines
and neurodegenerative disorders. Gaining insight into how circadian rhythms, Ramadan
fasting, and migraines interact can shed light on migraine triggers and management strategies during
this period. This review synthesizes current literature on the relationship between circadian
rhythms and migraines, examines possible mechanisms, and suggests approaches for managing migraines
during Ramadan fasting while accounting for circadian rhythm disturbances.
Characterizations of Degree-based Topological Indices in Boron-embedded Benzenoid Graphs
Authors: Liu Yuanyuan , Sun Weigang 
Published: January 2025

Background:
Chemical graph theory is a crucial tool for characterizing
molecular properties and reactions. It utilizes a rigorous mathematical framework to
reveal the complex structures and dynamics of molecules.

Methods:
The atomic structure of boron is incorporated into an n-dimensional oxide
network to create two sets of boron-embedded benzenoid networks. By employing
mathematical analysis and graph theory, degree-based topological indices are derived.

Results:
Analytical solutions for molecular descriptors of degree-based topological
indices in boron-embedded benzenoid networks are computed.

Conclusion:
The unique structures of boron-embedded benzenoid networks significantly
influence the topological indices, highlighting the interplay between molecular
structures.
Artificial Intelligence in Pharmacovigilance
Authors: Kumar Dinesh , Kaur Amandeep , Shruti  , Kaur Davender 
Published: January 2025

Abstract:
Pharmacovigilance (PV) is a data-driven method that quickly identifies medication
safety risks by processing reports of suspected Adverse Events (AEs) and extracting health data.
The first steps in the PV case processing cycle include data collection, data entry, coding, preliminary
validity and completeness checks, and medical evaluation for severity, seriousness, expectation,
and causality. Afterward, a report is submitted, quality is checked, and data storage and
maintenance are performed. This process is costly and time-consuming, as it requires both a workforce
and technology. Conversely, Artificial Intelligence (AI) is used to reduce this time investment
and increase data accuracy. AI includes machine learning methods like deep learning and
natural language processing, which can recognize and retrieve information on adverse drug occurrences.
By doing so, it is possible to optimize the pharmacovigilance process and improve the
tracking of documented adverse medication occurrences. AI's advancement in pharmacovigilance
raises concerns about potential changes in drug safety professionals' roles, prompting curiosity
about their future in an AI-assisted workplace. Artificial Intelligence (AI) should augment human
intelligence, not replace human specialists. It's crucial to highlight and ensure AI improves PV
more than it causes problems. The pharmaceutical business faces significant obstacles and opportunities,
especially when it comes to implementing and employing advanced Information Technology
(IT) in Pharmaceutical Monitoring (PMS) Systems. Automation improves PV in several
ways (e.g., boosting data quality or improving consistency). Several themes are discussed, outlining
the challenges encountered, exploring potential solutions, and emphasizing the need for further
research. The accepted use case involves automating the workflow in the case of ICRS.
BIOPORE INFILTRATION HOLES WITH ORGANIC FERTILIZER FOR ENHANCING SOIL NUTRIENTS AND COFFEE PRODUCTION IN ACIDIC CLAY SOILS
Authors: Jauhary Muhammad Rifqi Al , Soemarno  , Munir Mochammad 
Published: January 2025

The decreasing land area for coffee cultivation in Indonesia has led to a reduction in coffee production. The availability of land with potential for coffee plantation is also decreasing. As a consequence, coffee is cultivated on less fertile lands, such as lands dominated by clay fraction. This land generally has a low pH so that the nutrient content is more absorbed in Al and Fe which react in an acidic environment. Furthermore, heavy texture of soil also inhibits root growth, making it difficult for plants to absorb nutrients. In this study, biopore infiltration holes were used as a solution to improve acidic soil with a dominant clay fraction. The research was conducted at Agrotechnopark Jatikerto, Malang Regency and the soil analysis was conducted at the Soil Chemistry Laboratory, Faculty of Agriculture, University of Brawijaya during 2020-2022. This research was conducted experimentally with a group randomized design. There were four treatments in this study, namely 1) control/no biopore, 2) 30 cm empty biopore, 3) 30 cm biopore + 2.12 kg compost fertilizer, and 4) 30 cm biopore + 1.05 kg goat manure fertilizer. The results showed that biopore infiltration holes added with compost or goat manure fertilizer were able to increase pH, soil organic C, CEC, BS, and total N-P-K nutrients. Furthermore, the increase in organic C and total N-P-K nutrients in the study area was also directly proportional to the increase in chlorophyll content and coffee production. The increase in chlorophyll content and coffee production is a result of the increase in chlorophyll content and coffee yield.
Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study
Authors: Wac Marceli , Santos-Rodriguez Raul , McWilliams Chris , Bourdeaux Christopher 
Published: January 2025

Background
Increasing use of computational methods in health care provides opportunities to address previously unsolvable problems. Machine learning techniques applied to routinely collected data can enhance clinical tools and improve patient outcomes, but their effective deployment comes with significant challenges. While some tasks can be addressed by training machine learning models directly on the collected data, more complex problems require additional input in the form of data annotations. Data annotation is a complex and time-consuming problem that requires domain expertise and frequently, technical proficiency. With clinicians’ time being an extremely limited resource, existing tools fail to provide an effective workflow for deployment in health care.

Objective
This paper investigates the approach of intensive care unit staff to the task of data annotation. Specifically, it aims to (1) understand how clinicians approach data annotation and (2) capture the requirements for a digital annotation tool for the health care setting.

Methods
We conducted an experimental activity involving annotation of the printed excerpts of real time-series admission data with 7 intensive care unit clinicians. Each participant annotated an identical set of admissions with the periods of weaning from mechanical ventilation during a single 45-minute workshop. Participants were observed during task completion and their actions were analyzed within Norman’s Interaction Cycle model to identify the software requirements.

Results
Clinicians followed a cyclic process of investigation, annotation, data reevaluation, and label refinement. Variety of techniques were used to investigate data and create annotations. We identified 11 requirements for the digital tool across 4 domains: annotation of individual admissions (n=5), semiautomated annotation (n=3), operational constraints (n=2), and use of labels in machine learning (n=1).

Conclusions
Effective data annotation in a clinical setting relies on flexibility in analysis and label creation and workflow continuity across multiple admissions. There is a need to ensure a seamless transition between data investigation, annotation, and refinement of the labels.
The Building of an Effective Post-Interpreting Review Framework—A Case Study Based on Two Interpreting Tasks of the 2020 WAIC Conference
Authors: Zou Hanshu 
Published: January 2025

The quality of interpreting is a key indicator of an interpreter's performance. An effective way to improve it is constant reflection. Therefore, post-interpreting review plays a crucial role in improving interpreting quality. At present, there are few studies on this area, and there is still a lack of a complete post-interpreting review framework. Specific studies on the tools, stages and contents of post-interpreting review are rare.Reflective learning is significant in improving learning effectiveness, with post-interpreting review as a key reflection activity in it. Therefore, reflective learning theories serves as an important guide to promote interpreting learning. Relevant studies have found that writing reflective journals can effectively improve the interpreting quality. In light of it, it is necessary to establish an effective post-interpreting review framework with the reference of reflective learning theories.Based on relevant reflective learning theories, a review framework was initially established to guide the first post-interpreting review and the second interpreting practice. By comparing the two tasks, the quality of interpreting and the awareness of reflecting was significantly enhanced. Both tasks are mock interpreting tasks for the 2020 World Congress on Artificial Intelligence. This shows that with the aid of a post-interpreting review framework, problems arising from the interpreting practice can be analyzed more comprehensively, and steady improvement of interpreting ability can be achieved.