bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ammonium nutrition enhances rhizosphere mobilization and uptake of silicon in white lupin grown in low phosphorus soil
Authors: Kostic Ljiljana , Pavlovic Jelena , Bosnic Predrag , Kostic Igor , Trailovic Maja , Dubljanin Tijana , Nikolic Miroslav 
Published: September 2024

Abstract

Background and Aims
While nitrogen (N) supply can enhance plant silicon (Si) accumulation, the mechanisms by which different forms of N affect Si mobilization in the rhizosphere are not well understood. This study aims to elucidate how pH changes induced by ammonium (NH4+) and nitrate (NO3−) affect Si availability in the rhizosphere, especially under low phosphorus (P) conditions.
Methods
White lupin (Lupinus albus) plants were grown in non-fertilized low-P soil, supplied with a low dose of N, either as NH4+ or NO3−, with or without supply of monosilicic acid. We measured Si levels in various rhizosphere soil pools, along with different plant and rhizosphere soil parameters.
Results
The addition of NH4+ significantly lowered rhizosphere pH and decreased both Si adsorbed to pedogenic Fe/Mn oxides and amorphous phytogenic Si, resulting in higher concentrations of plant available Si in the white lupin rhizosphere. This led to greater Si uptake and improved plant growth compared to both the –N and + NO3− treatments. The supply of Si further enhanced these effects, with NH4+ showing a consistently different pattern of influence compared to NO3−. Additionally, –N white lupin plants accumulated more P than those treated with N, while Si supply significantly improved P acquisition across all treatments.
Conclusions
Our study demonstrates that rhizosphere acidification induced by NH4+ nutrition can significantly enhance Si mobilization from the rhizosphere soil in the absence of Si supply and reduce Si adsorption when Si is applied. These findings may have practical implications for improving both Si mobilization in the rhizosphere and the effectiveness of Si fertilizers.
Further Analysis of "Lattice-based key agreement protocol under ring-LWE problem for IoT-enabled smart devices"
Authors: Saeidi Mohammad Reza , Mala Hamid 
Published: September 2024

Abstract

The increasing interconnectedness of devices via the Internet of Things (IoT) highlights the growing importance of addressing IoT security issues. Authenticated Key Agreement (AKA) protocols ensure secure communication between IoT devices, users, and central servers. Preserving anonymity in AKA protocols is especially crucial given privacy issues and user preferences. The emergence of Shor's algorithm in 1994 and recent advancements in quantum computing have posed a threat to the security of classical AKA schemes relying on integer factorization, discrete logarithm, and other number theory problems. Consequently, researchers have turned their attention to developing quantum-resistant AKA schemes. In this context, Rana and Mishra introduced a lattice-based key agreement protocol under ring-LWE problem for IoT-enabled smart devices. However, a thorough security assessment uncovered vulnerabilities in their protocol, specifically susceptibility to replay and Key Compromise Impersonation (KCI) attacks. Furthermore, their approach reveals potential weaknesses in user impersonation if the user's identity is compromised.
Noise Mapping, Identification of Hot Spots, and Mitigation Plan for Control of Noise Pollution for Ahmedabad
Authors: Singh Bhupendra P. , Kalra Chahat , Akthar Nasim 
Published: September 2024

Abstract

Worldwide, urban centres are facing challenges due to road traffic, leading to increased noise pollution that impact residents' quality of life and the environment. This study presents a comprehensive analysis of noise pollution levels in Ahmedabad, India, across different urban zones (residential, commercial, and industrial) and times of day, utilizing QGIS software to generate noise heat maps. The noise level was measured at 133 locations in different areas of the city. The monitoring was performed using a digital sound level meter and a noise map was produced using the recorded equivalent energy noise level values. The mean noise levels were recorded consistently high, with daytime levels averaging 79.88 dB, nighttime levels at 74.76 dB, and late-night levels at 61.47 dB, respectively. Industrial areas recorded the highest noise levels, followed by commercial and residential zones. A comparative analysis with similar studies across India and globally indicates that Ahmedabad’s noise pollution exceeded the levels reported in other major urban centers. The correlation analysis calculated a strong correlation between noise levels in different zones, particularly between residential and industrial areas was observed, suggesting the widespread impact of noise pollution across the city. The findings underscore the urgent need for targeted noise management strategies, including stricter enforcement of noise regulations, urban planning that minimizes noise exposure in residential areas, and the implementation of noise barriers and soundproofing measures in industrial and commercial zones. The study highlights the critical role of spatial analysis tools like QGIS in visualizing noise distribution and informing public policy.
Variations and trends in the stand structure and structural complexity of selected mangrove stand types in the Philippines
Authors: Gianan Eunice Lois D. , Yñiguez Aletta T. , Salmo Severino G. 
Published: September 2024

Abstract

The declining mangrove forest cover in the Philippines led to increased attention towards their conservation and restoration, driven by growing awareness of their importance and the ecosystem services they provide. Yet, time-series information on forest structure, growth, and development of naturally occurring and planting efforts is still lacking. The conventional "complexity index" (Ic) rapidly characterizes forest structure using species richness and mean values (of only the top three tallest trees ≥ 10 cm in diameter for height, basal area, and density). However, it does not capture unique mangrove characteristics like canopy diameter and spatiotemporal growth dynamics. Mangroves often have spread-out canopies with varying tree diameters in response to environmental conditions. This study used a modified complexity index (Icm) that integrates foliage and all tree parameters' mean values to assess mangrove structural complexity. We assessed and compared Ic and Icm (at five to seven-month intervals) on selected mangrove stands in the Philippines composed of the natural and planted stands of Bani, Pangasinan; the natural stands of Calapan, Oriental Mindoro; and the natural stands of Las Piñas-Parañaque Critical Habitat and Ecotourism Area (LPPCHEA), Metro Manila. Results showed that LPPCHEA and Calapan have intact stand structural features, while Bani stands are either in their secondary forest growth or disturbed stage. The LPPCHEA had the highest structural complexity regardless of the method used, followed by Calapan, Bani-natural, and Bani-planted. There was high variability across stands and time, but the Icm was 4x higher than the Ic and is more sensitive to detecting growth variability between stand types and sampling periods. Overall, the selected Philippine mangrove stand types showed higher structural complexity than those from neighboring countries. This study suggests that mono-species stands are less structurally complex than multi-species stands. Our results imply the need for (1) better assessment of the stability of structural complexity in mature mangroves, (2) better tracking of the structural complexity (in a chronosequence) in restored mangroves, and (3) better detection of the impacts of disturbance (e.g., typhoons) and post-disturbance recovery of structural complexity.
An Incremental Entity Resolution Approach based on Deep Metric Learning
Authors: Xu Yaoli , Li Chenglin , Li Pu , Duan Xiaoyu , Wang Yanyan , Qi Wanhua , Guo Yanbu , Li Yuhua , Shi xiayang , Zhang Suzhi 
Published: September 2024

Abstract

Entity resolution (ER), a crucial component of data fusion, aims to identify and aggregate disparate entity descriptions describing the same real-world object. While existing approaches have achieved good performance in ER tasks, they fall short in scenarios with dynamic data sources. The inherent dynamism of these data sources requires ER models to progressively resolve entities with incoming data while maintaining high accuracy. To address such issue, we propose a novel dynamic data processing framework that leverages the matching model from the previous time step to parse incoming incremental data, and implement an Incremental Entity Resolution Approach based on Deep Metric Learning (IER-DML), developed to handle dynamic data and enhance matching capability. an uncertainty metric-based data augmentation mechanism is proposed to mitigate calibration issues caused by suboptimal training data, and to generate high-confidence labeled entity pairs. These labeled data are then used for the iterative optimization of IER-DML, enhancing ER capabilities of IER-DML. For modeling dynamic changes in the distributions of clustering structure among entity pairs, we propose a deep metric learning-based multi-center matcher. This matcher learns metric relationships between the deep semantic features of entity pairs, and models intrinsic structures using "soft centers". These centers capture multiple information clusters among entities, and flexibly represent clustering structures. By integrating deep metric learning with a multi-center strategy, it can dynamically adjust classification boundaries and metric standards in complex scenarios. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art methods across all four dynamic benchmark datasets.
Improved photocatalytic activity of Ce-doped NiO nanoparticles against methylene blue and rhodamine B dyes
Authors: Kistan A. , Priya G. Hari Hara , Raj S. Jagan , Mayavan L. 
Published: September 2024

Abstract

The current study intended to investigate the photocatalytic efficiency of synthesised pure and Ce-doped NiO nanoparticles for the degradation of organic contaminants, particularly Methylene blue and Rhodamine B dyes. Initially, the co precipitation method was used to synthesize these nanoparticles.The size of the crystallites was determined using X-ray diffraction analysis,which also verified that the prepared nanoparticles included a single cubic phase. In addition, several characterisation techniques were used to assess the band gap energy, functional group, phase identification, shape, surface area, and oxidation states of the synthesised nanoparticles' elements. These techniques included UV-vis, FT-IR,FESEM, TEM, BET, and XPS. It was demonstrated that Ce-doped NiO nanoparticles had a 98% and 79% degradation efficiency for MB and RhB dyes respectively.
Modulation of noradrenergic signalling reverses stress-induced changes in the hippocampus: involvement of orexinergic systems
Authors: Sarfi Masoumeh , Salmani Mahmoud Elahdadi , Lashkarbolouki Taghi , Goudarzi Iran 
Published: September 2024

Abstract

Stress can be beneficial for adapting to dangerous situations in the short term, but can be damaging in the long term, especially in the hippocampus. The noradrenergic and orexinergic systems play important roles in the stress response. This study investigated the effect of noradrenergic activation on the changes induced by chronic stress in the hippocampus and the involvement of orexinergic modulation in this process. Twenty male Wistar rats were subjected to chronic stress, acute stress, administration of α2 receptor antagonist yohimbine, or their combinations. Plasma corticosterone (CORT) was measured using a fluorometric method. Expression of prepro-orexin (prepro-OX), orexin receptor-1 (OXr1), and glucocorticoid receptor (GR) was analyzed using RT-PCR. Neuronal populations were quantified using Nissl staining. Chronic and acute stress increased plasma CORT levels, gene expression of prepro-OX, OXr1, and GR, while decreasing neuronal number, with the chronic stress having a more pronounced effect. The stress- induced and Yohimbine treated groups demonstrated a higher level of plasma CORT. Chronic stress substantially increased prepro-OX expression, while yohimbine recovered the profile in chronically stressed animals. OXr1 expression was profoundly higher in the chronic stress group, while chronic stress combined with yohimbine decreased that profile. Similarly, chronic stress upregulated glucocorticoid receptor, while chronic stress combined with yohimbine reversed the effect. Conversely, the chronic stress reduced hippocampal neuronal populations and chronic stress combined with yohimbine partially compensated the neuronal numbers compared to chronic stress alone. The results suggest that noradrenergic signalling can reverse the chronic stress-induced impairments in prepro-OX, OXr1, GR, and neuronal population.
Escape of Kdm6a from X chromosome is detrimental to ischemic brains via IRF5 signaling
Authors: Ngwa Conelius , Misrani Afzal , Manyam Kanaka Valli , Xu Yan , Qi Shaohua , Sharmeen Romana , McCullough Louise , Liu Fudong 
Published: September 2024

Abstract

The role of chromatin biology and epigenetics in disease progression is gaining increasing recognition. Genes that escape X chromosome inactivation (XCI) can impact neuroinflammation through epigenetic mechanisms. Our prior research has suggested that the X escapee genes Kdm6a and Kdm5c are involved in microglial activation after stroke in aged mice. However, the underlying mechanisms remain unclear. We hypothesized that Kdm6a/5c demethylate H3K27Me3/H3K4Me3 in microglia respectively, and mediate the transcription of interferon regulatory factor 5 (IRF5) and IRF4, leading to microglial pro-inflammatory responses and exacerbated stroke injury. Aged (17–20 months) Kdm6a/5c microglial conditional knockout (CKO) female mice (one allele of the gene) were subjected to a 60-min middle cerebral artery occlusion (MCAO). Gene floxed females (two alleles) and males (one allele) were included as controls. Infarct volume and behavioral deficits were quantified 3 days after stroke. Immune responses including microglial activation and infiltration of peripheral leukocytes in the ischemic brain were assessed by flow cytometry. Epigenetic modification of IRF5/4 by Kdm6a/5c were analyzed by CUT&RUN assay. The demethylation of H3K27Me3 by kdm6a increased IRF5 transcription; meanwhile Kdm5c demethylated H3K4Me3 to repress IRF5. Both Kdm6afl/fl and Kdm5cfl/fl mice had worse stroke outcomes compared to fl/y and CKO mice. Gene floxed females showed more robust expression of CD68 in microglia, elevated brain and plasma levels of IL-1β or TNF-α, after stroke. We concluded that IRF5 signaling plays a critical role in mediating the deleterious effect of Kdm6a; whereas Kdm5c’s effect is independent of IRF5.
Air quality in the U.S. during the 2023 wildfire season
Authors: Dinavahi Siddhi , Archer Cristina 
Published: September 2024

Abstract

During the summer of 2023, Canada was devastated by a series of large and uncontrolled wildfires that set the unprecedented record of over 40 million acres burned and over 120,000 people evacuated. The smoke from the Canadian wildfires reached the U.S. and negatively affected the air quality in several states. The goal of this paper is to document the U.S. air quality in 2023 within the historical and regulatory contexts; more specifically, to compare the concentrations of fine particulate matter (PM2.5) and tropospheric ozone (O3), assessed using statistics as close as possible to those recommended for comparison against the National Ambient Air Quality Standards (NAAQS), in 2023 against those in the past 20 years. We report that the year 2023 was an anomaly, meaning that the well-established trend of decreasing concentrations and improving air quality was suddenly reversed in 2023, with national-median PM2.5 and O3 concentrations exceeding \SI{33}{\micro\gram\per\cubic\metre} and 75 parts per billion (ppb), respectively. These values had not been reached since 2007 and 2012 for PM2.5 and O3, respectively. Notably, the 75th percentile for PM2.5 was the highest and the 90th percentiles was the third highest in 2023 since 2000. Out of 50 states (plus the District of Columbia), 34 and 49 experienced summer-average concentrations in 2023 that were higher than those in 2013--2022 for PM2.5 and O3, respectively. For PM2.5, the states that experienced the highest median concentrations were: California, Alaska, Oregon, and Idaho with 95, 64, 52, and \SI{50}{\micro\gram\per\cubic\metre}; for O3, they were: California, Maryland, Texas, Connecticut, and Pennsylvania with 114, 88, 88, 86, and 85 ppb. In summary, in the year 2023 the air quality in the U.S. was the worst of the past 15 and 10 years in terms of PM2.5 and O3, respectively.
A Selection Method of Agricultural Product Price Forecasting Model based on Time Series Image Encoding
Authors: Jiang Wentao , Wang Quan , Li Hongbo 
Published: September 2024

Abstract

The forecasting and early warning of agricultural product price time series is an important task in the field of stream data event analysis and agricultural data mining. The existing forecasting methods of agricultural product price time series have the problems of low precision and low efficiency. To solve these problems, we propose a forecasting model selection method based on time-series image encoding technology. Specifically, we use Gramian Angle fields (GAFs), Markov transition fields (MTF), and Recurrence Plots (RP) to encode time series to images and retain all information about the event. Then, we propose a information fusion feature augmentation method (IFFA) to combine time series images. The time series combined images(TSCI) are input into the CNN forecasting model selection classifier. Finally, we introduce the idea of transfer learning to optimize the selection method of agricultural product price time series forecasting model. This idea can effectively reduce the overfitting phenomenon caused by insufficient data or unbalanced samples in real data set. Experimental results show that, compared with the existing methods, our IFFA-TSCI-CNN time series classification method has great advantages in efficiency and accuracy.
Burnout Prevalence Among Postgraduate Medical Trainees in the Eastern Health Cluster, Saudi Arabia: A Cross-Sectional Study
Authors: Alzain Nasser M. , Almsned Fahad , AlBunaian Nada A. , Alotaibi Fahad , AlBeshir Mohammad S. , Yosef Mostafa , Ali Moatazbellah I. 
Published: September 2024

Abstract

Background: Burnout among medical professionals is recognized globally, with significant implications for mental health and patient care. This study evaluates the prevalence of burnout among postgraduate medical trainees in the Eastern Health Cluster of Saudi Arabia, aiming to fill the research gap in this region, where specific cultural and training factors may contribute uniquely to burnout.
Methods: A cross-sectional study was conducted from March to June 2024 across various healthcare facilities within the Eastern Health Cluster. A total of 422 postgraduate medical trainees participated, providing data through a Google Form survey, which included the Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI-HSS(MP)).
Results: The study found a high prevalence of burnout symptoms among trainees: 30.3% reported high levels of emotional exhaustion, 24.9% reported high levels of depersonalization, and a significant 86.3% felt low levels of personal accomplishment. Burnout rates varied significantly across specialties and training levels, suggesting targeted interventions were needed.
Conclusions: The high prevalence of burnout underscores the critical need for tailored preventive strategies and interventions within the training environments to effectively address and mitigate burnout among medical trainees. Future research should explore longitudinal interventions to reduce burnout and enhance trainee well-being.
Assessment of Apoptotic Effect of Carbamazepine on Tongue Squamous Cell Carcinoma Cell Line (in vitro Study)
Authors: Bahaa Rahaf Abdel Wahab , Abdellatif Mohsen Kazem , Elhalawany Mai , Saleh Heba Ahmed 
Published: September 2024

Abstract

Background
This study investigates the apoptotic and anti-cancerous effects of carbamazepine on the tongue squamous cell carcinoma (HNO-97) cell line.
Methods
The research compares carbamazepine-treated HNO-97 cells with untreated HNO-97 cells as a control. The Hematoxylin and Eosin staining method was used to observe cellular morphological changes. MTT assay was performed to assess cell viability. ELISA technique was done to measure reactive oxygen species and p53 upregulated modulator for investigating the drug apoptotic and potential anticancer effect.
Results
Thestudy findings indicate that carbamazepine reduces cell viability in a concentration-dependent manner via induction of apoptosis by increase production of reactive oxygen species and p53 upregulated modulator as they led finally to induction of apoptosis in oral cancer cells, which were confirmed by ELISA. Prominent cellular apoptotic signs were detected microscopically for the oral cancer cells in both early and late stages of apoptosis.
Conclusion
The study findings suggested the potent apoptotic and anti-cancerous effect of carbamazepine on oral squamous cell carcinoma cells through the activation of the caspase apoptotic pathway. This drug may serve as a promising adjuvant therapy for oral cancers.
The Diffusion Interaction Region of Dayside Magnetic Reconnection
Authors: Beedle Jason , Gershman Daniel , Shuster Jason , Uritsky Vadim , Phan Tai , Norgren Cecilia , Burch James , Genestreti Kevin , Heuer Steven , Giles Barbara , Torbert Roy 
Published: September 2024

Abstract

Magnetic reconnection is a universal process that allows for the transfer and release of energy previously stored in a magnetic field configuration. At Earth, this process can occur in the boundary layer between Earth's magnetic field and the solar wind, called the magnetopause. This allows the magnetopause to act as the primary ``entry gate" for the solar wind's energy into the Earth’s magnetosphere and upper atmosphere. Along the magnetopause, magnetic reconnection is triggered inside diffusion regions: an electron diffusion region (EDR) embedded inside a larger ion diffusion region (IDR). Inside these diffusion regions, plasma decouples from the magnetic field, allowing the release of magnetic energy into the plasma. We identify a hitherto undefined region in the diffusion regions, which results from the interaction of demagnetized electrons in the EDR's reconnection current sheet and remagnetizing electrons in the IDR. We call this region the Diffusion Interaction Region (DIR). Using in situ MMS plasma data and 2.5D particle-in-cell simulations, we isolate its defining features, which includes a significant parallel current structure and regions of parallel energy conversion. Defining this important and underexplored region of magnetic reconnection helps us determine how the local diffusion regions may couple with the large-scale current structures within which they are embedded.