bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Environmentally Compatible and Less Costly (Greener) Microwave Digestion Method of Bone Samples Using Dilute Nitric Acid for Analysis by ICP-MS
Authors: Bussan Derek D. , Nielsen Forrest H. , Douvris Chris , Kelzenberg Brett , Grimestad Allison , Cao Jay J. 
Published: August 2024

Abstract

An environmentally compatible and less costly (green) analytical method for the digestion of bone meal samples using microwave-assisted dilute nitric acid (HNO3) was developed and optimized. The method employing a mixture of 1 mL concentrated HNO3 and 4 mL of deionized water gave a comparable performance to the conventional method using 5 mL of concentrated HNO3. Accuracy of the method was validated by using certified reference material NIST 1486 (Bone Meal); percent recoveries were within ± 15% for all eight certified elements. Statistical analysis revealed no significant differences (p > 0.05) in percent recoveries between the green and conventional methods for all elements except calcium. The greenness of the developed method was evaluated by using the analytical Eco-Scale, achieving a score of 87, categorizing it as an "excellent green analysis" method. This research highlights the potential for adopting greener practices in trace element analysis that reduce environmental impact and safety risks associated with concentrated acids.
Understanding the effects of transportation and perceived built environment on community and individual well-being in the United States
Authors: Khan Muhammad Asif , Godavarthy Ranjit Prasad , Motuba Diomo , Mattson Jeremy 
Published: August 2024

Abstract

This research study aimed to evaluate the impact of transportation, specifically public transit, perceived built environment, and sociodemographic indicators on community quality of life (QoL) and individual’s overall life satisfaction (OLS) at the national level in the United States. The study used survey data collected at the national level in the United States in 2017 called the National Community Livability Survey (NCLS). The surveys were sent out to 25,000 adults across 50 U.S. states, with 994 completed responses received. The structural equation modeling (SEM) technique was employed to investigate the effects of three latent factors, i.e., perceived built environment, quality of transportation, and support for transit funding on community QoL and individual OLS. Along with latent factors, observed variables included in the analysis were perceived public transit importance, public transit need, ease of travel, and sociodemographic indicators. The perceived built environment and quality of transportation have a significant positive impact on community QoL but an insignificant impact on an individual’s OLS. Support for transit funding was found to have an insignificant effect on community QoL but a significant positive impact on individual OLS. Perceived public transit importance has a significant negative impact on community QoL, while public transit needs have a significant negative effect on an individual’s OLS. Ease of travel was the only variable with a significant positive impact on both community QoL and individual OLS. Sociodemographic indicators such as age, income, health, education, and race significantly affect an individual’s satisfaction with life. Improvement in the perceived built environment, quality of transportation, specifically public transit and active transportation, and providing easily accessible travel options may improve the QoL of people.
Automatic Smishing Detection System with Feedback Loops
Authors: Gallet Guillaume , Guerard Guillaume , Djebali Sonia 
Published: August 2024

Abstract

This study delves into the escalating issue of Smishing, an emerging menace within the information security landscape spurred by the widespread use of text messages on smartphones. Smishing, a portmanteau of SMS and Phishing, involves attackers disseminating SMS with malicious content to unsuspecting victims. This content often harbors links that redirect users to websites housing malicious applications and deceptive user interfaces. The primary goal of this study is to construct an artificial intelligence-based Anti-Smishing filter capable of detecting and thwarting Smishing attempts. The societal significance of this research is underscored by the imperative to safeguard personal information in the face of a continuously evolving threat landscape. The detrimental impact of Smishing on individual security, encompassing both financial and personal realms, underscores the necessity for innovative solutions. Addressing this issue necessitates the development of a machine learning model specifically tailored to identify the distinct characteristics of Smishing. While the final results are pending, the strides made thus far indicate a substantial contribution toward the establishment of an effective filter. This research project is poised to yield tangible solutions to counteract Smishing, thereby fortifying the security of personal information in the global context of mobile communications. By creating a robust defense mechanism against Smishing attempts, this initiative aligns with the broader objective of enhancing cybersecurity and preserving the integrity of personal data in an increasingly interconnected and vulnerable digital environment.
A Novel Battery Less Regulated Output Scheme for a Hybrid Solar Photo Voltaic and Thermoelectric Generation System
Authors: D Poornima , C Vivekanandan 
Published: August 2024

Abstract

This article introduces a new hybrid system that combines solar photovoltaic (SPV) and thermoelectric generator (TEG) sources for delivering a regulated output. The main power source is the SPV, which is connected to a boost converter. The TEG acts as a secondary source. In a conventional system without a storage provision like a battery, fluctuations in sunlight could impact the power delivered to the load. In the proposed system, the TEG quickly compensates for these fluctuations, ensuring a stable output voltage across the load or a steady current through a fixed resistive load. This system eliminates the need for a storage battery to maintain a regulated output. The SPV power is optimized using a Sliding Mode Controlled (SMC) maximum power point tracking (MPPT) boost converter. The TEG section, controlled by another SMC, uses a buck converter. The output voltages from both converters are combined and applied across the load. Simulation results in MATLAB and experimental prototypes confirm that this system effectively compensates for real-time changes in solar irradiance, making it an efficient hybrid solar power system.
Single-cell mapping of metabolic dependences using ATP as a biomarker
Authors: McGuire Peter , Marin Jose , Fuchs Amanda , Tarasenko Tatiana , Warren Emily , Kirby Martha , Anderson Stacie , Gordon-Lipkin Eliza , Kruk Shannon , West A 
Published: August 2024

Abstract

Metabolic dependences highlight a cell's reliance on specific pathways to meet its bioenergetic needs, with these pathways being interrogated using chemical inhibitors to assess their significance. While surrogate markers of bioenergetics (e.g., oxygen consumption) have yielded important insights, we asked whether metabolic dependences could be defined using ATP as a biomarker. To address this gap, we developed Mitochondrial/Energy Flow Cytometry (MitE-Flo), a method that evaluates the contributions of glycolysis, fatty acid oxidation (FAO), and oxidative phosphorylation (OXPHOS) to cellular ATP content. In models of mitochondrial disease due to complex I or complex IV deficiency, we identified impaired OXPHOS with a compensatory shift to glycolysis. To define the utility of ATP monitoring in immunometabolism research, we analyzed previously inaccessible cell populations: light zone (LZ) and dark zone (DZ) germinal center (GC) B cells. Highly proliferative DZ B cells exhibited elevated ATP levels and a preference for FAO and OXPHOS over glycolysis, with uniform increased activity across ETC complexes. In contrast, less proliferative LZ B cells showed lower ATP levels and an equal reliance on glycolysis and OXPHOS. Using ATP as a biomarker to define metabolic dependences provides valuable insights into disease states and elusive immune cell subtypes, thereby enhancing the metabolism research toolkit.
Temporal and Spatial Analysis of PM2.5 Concentrations in Accra Using Integrated Ground-Based and Satellite Sensor Data
Authors: SOWAH RICHARD ADDO , AFEMEKU CONFIDENCE MAWUFEMOR 
Published: August 2024

Abstract

Air pollution, particularly fine particulate matter, poses a significant public health challenge in urban areas worldwide. This study presents a comprehensive temporal and spatial analysis of PM2.5 concentrations in Accra, Ghana, utilizing an integrated approach combining data from low-cost ground-based sensors, reference-grade monitors, and satellite-derived sources. Over a 21-month period from April 2019 to December 2020, PM2.5 levels were monitored across five strategic locations within the Greater Accra Metropolitan Area (GAMA). The findings reveal distinct seasonal trends, with higher PM2.5 concentrations during the dry season (November to March) and lower levels during the wet season (April to October). This seasonal variation underscores the influence of meteorological conditions on particulate matter levels. Spatial analysis indicates significant variations in PM2.5 concentrations, with higher levels recorded in densely populated and industrial zones such as Amasaman and Akweteyman. These areas are particularly affected by emissions from vehicular traffic, industrial activities, and biomass burning. A key outcome of this study is the strong positive correlation between ground-based sensor data and MERRA-2 satellite-derived PM2.5 concentrations, validating the use of satellite data to complement traditional ground-based monitoring. Despite some discrepancies, where satellite data tended to overestimate PM2.5 levels in 2020 and underestimate in 2019, the integration of these data sources provides a more robust and comprehensive assessment of air quality. The elevated PM2.5 levels observed in industrial and densely populated areas have significant public health implications. Prolonged exposure to high PM2.5 concentrations is linked to increased risks of respiratory and cardiovascular diseases. This study highlights the urgent need for targeted air quality management strategies and public health interventions to mitigate these risks. By demonstrating the feasibility and effectiveness of using low-cost sensors in combination with satellite-derived data, this research offers a scalable and cost-effective solution for air quality monitoring in resource-constrained settings. The insights gained from this study can inform policy-making and contribute to improved public health outcomes in Accra and similar urban environments.
Vagal Insulin Signaling Regulates Sympathovagal Balance to Determine Peripheral Metabolic and Inflammatory State
Authors: Woo Minna , Li Yu Zhe , Sung Joe Eon , Lee Ju Hee , Vo Nathaniel , Rapps Joshua , Pollock-Tahiri Evan , Ghebreselassie Martha , Chen Chuck , Premsigh Benny , Bazinet Richard , Billia Phyllis , Mak Tak , Sung Hoon-Ki , Hui Chi-chung , Yoo Paul 
Published: August 2024

Abstract

The vagus nerve (VN) is a major component of the parasympathetic nervous system that regulates vital functions, including glucose and energy homeostasis. However, the specific molecular signaling pathways within vagal neurons that regulate metabolism are unclear. Here, we show that enhanced PI3K signaling within the vagal neurons through the deletion of its endogenous negative regulator, phosphatase and tensin homolog (Pten), enhanced VN function along with decreased inflammation within the vagal neurons. This was associated with an anti-inflammatory state in peripheral metabolic tissues such as the adipose, liver and skeletal muscle, with protection against high fat diet (HFD) induced glucose intolerance and insulin resistance. This was driven at least in part by macrophages, such that their depletion led to abolishment of metabolic protection. Single nuclear RNA sequencing analysis of the neuronal clusters within the nodose ganglia of the VN revealed that Pten-deficiency lead to upregulation of genes involved in myelination and neuronal growth, in keeping with large myelinated nerve fibers conducive for improved VN function. Intriguingly, upregulation of genes involved in adrenergic signaling was present in these neurons which correlated with enhanced beiging and lipolysis, increased sympathetic nerve density and increased norepinephrine levels in the adipose tissue. Finally, to assess specific role of insulin signaling given multiple upstream stimuli that can activate PI3K signaling, we generated mice with Insulin receptor deficiency specifically in the vagal neurons. These mice exhibited reduced vagal function and exacerbated HFD-induced metabolic abnormalities and inflammation which was rescued with concomitant Pten deletion. Together, we show that insulin signaling within the vagal neurons plays an essential role in optimizing sympathovagal balance in determining peripheral inflammatory and metabolic homeostasis which can be harnessed as novel therapeutic strategy to combat diabetes and obesity.
Differential brainstem connectivity according to sex and menopausal status in healthy men and women
Authors: Kilpatrick Lisa A , Gupta Arpana , Meriwether David , Mahurkar-Joshi Swapna , Li Vince W , Sohn Jessica , Reist Juliana , Labus Jennifer S , Dong Tien , Jacobs Jonathan P , Naliboff Bruce D , Chang Lin , Mayer Emeran A 
Published: August 2024

Abstract

Background
Brainstem nuclei play a critical role in both ascending monoaminergic modulation of cortical function and arousal, and in descending bulbospinal pain modulation. Even though sex-related differences in the function of both systems have been reported in animal models, a complete understanding of sex differences, as well as menopausal effects, in brainstem connectivity in humans is lacking. This study evaluated resting-state connectivity of the dorsal raphe nucleus (DRN), right and left locus coeruleus complex (LCC), and periaqueductal gray (PAG) according to sex and menopausal status in healthy individuals. In addition, relationships between systemic estrogen levels and brainstem-network connectivity were examined in a subset of participants.
Methods
Resting-state fMRI was performed in 50 healthy men (age, 31.2 ± 8.0 years), 53 healthy premenopausal women (age, 24.7 ± 7.3 years; 22 in the follicular phase, 31 in the luteal phase), and 20 postmenopausal women (age, 54.6 ± 7.2 years). Permutation Analysis of Linear Models (5000 permutations) was used to evaluate differences in brainstem-network connectivity according to sex and menopausal status, controlling for age. In 10 men and 17 women (9 premenopausal; 8 postmenopausal), estrogen and estrogen metabolite levels in plasma and stool were determined by liquid chromatography-mass spectrometry/mass spectrometry. Relationships between estrogen levels and brainstem-network connectivity were evaluated by partial least squares analysis.
Results
Left LCC-executive control network (ECN) connectivity showed an overall sex difference (p = 0.02), with higher connectivity in women than in men; however, this was mainly due to differences between men and pre-menopausal women (p = 0.008). Additional sex differences were dependent on menopausal status: PAG-default mode network (DMN) connectivity was higher in postmenopausal women than in men (p = 0.04), and PAG-sensorimotor network (SMN) connectivity was higher in premenopausal women than in men (p = 0.03) and postmenopausal women (p = 0.007). Notably, higher free 2-hydroxyestrone levels in stool were associated with higher PAG-SMN and PAG-DMN connectivity in premenopausal women (p < 0.01).
Conclusions
Healthy women show higher brainstem-network connectivity involved in cognitive control, sensorimotor function, and self-relevant processes than men, dependent on their menopausal status. Further, 2-hydroxyestrone, implicated in pain, may modulate PAG connectivity in premenopausal women. These findings may relate to differential vulnerabilities to chronic stress-sensitive disorders at different life stages.
Breeding Strategies for the Apiaceae family: Utilizing Molecular Markers to Improve Agronomic Traits
Authors: Mistry Vishal J. 
Published: August 2024

Abstract

One of the most significant taxonomic groups of flowering plants, the Apiaceae family has thousands of species utilized in food production, flavoring, fragrance, medicine and industry. The primary goal of this study was to examine genomics and transcriptomic data available for this family and their use for the constitution of new varieties. Stress is an environmental factor genetically. High heritability coupled with high genetic advance as per cent over mean was observed for economical traits (85.5 and 34.7% for leaf yield, respectively), which indicated that the traits were highly heritable in nature; hence selection breeding is most effective. These markers are classified into various groups based on how the markers are used, RAPD (Random Amplified Polymorphic DNA) markers serve to identify and screen hybrids based on salinity and drought stress tolerance, while Simple Sequence Repeat (SSR) markers are excellent for the assessment of stress tolerance. Such markers also play an important role in the QTL (quantitative trait loci) mapping of stress-related genes. Advantages such as rapidity, non-interference by the environment, and accuracy during selection have made marker-assisted selection the most reliable tool for identifying agronomically important traits. This review outlines the general characteristics of some important DNA markers and current information about how to use them in marker-assisted selection. Historical milestones in plant breeding for 10,000 years, farmers and breeders have been developing and improving crops.
Water quality assessment of Dudhganga river using water quality index and anthropogenic activities
Authors: Lagade Vishwajeet Mahadev , Taware Shital Shantaram , Lagade Swapnaja Vishwajeet 
Published: August 2024

ABSTRACT

The primary goal of this study is to analyze magnitude of water quality of Dudhganga River seasonally and temporally using physico-chemical parameters, Weighted Arithmetic Water Quality Index (WA-WQI) and anthropogenic activities of Kolhapur District, Maharashtra. Six sampling locations of Dudhganga River (sites I to VI) were selected for sampling and analysis. In seasonal analysis, higher values of water parameters like pH (8.2), TDS (0.42 μs/cm), turbidity (0.14 NTU), DO (6.24 mg/l), nitrate (7.56 mg/l) and phosphate (1.78mg/l) were reported in monsoon season. Temperature, alkalinity and hardness were noted maximum values (25 °C, 66.67 mg/l and 12.67 mg/l) during summer season. However, EC was reported maximum (64 μs/cm) in winter season. Results of WA-WQI revealed that water quality index ranged from 14.90 to 24.26 all sampling sites. The values of WA-WQI were reported higher at downstream locations like IV, V, and VI, than upstream sites I, II, and III. Along Dudhganga River various human activities were substantial contributors for declining the river water quality. The acquired results suggest that water quality status is close to becoming contaminated and unsafe for domestic and human use. Such contaminated water also poses risks to plants and animals that make up the aquatic ecosystem.
Age-dependent dose assessment of uranium exposure in groundwater of Ganderbal and Budgam districts in Jammu and Kashmir
Authors: Rahim Asiya , Khan Savidh , Mehra Rohit , Chand Sansar , Majeed Ikhlaq , Ahmed Rayees 
Published: August 2024

ABSTRACT

Using an LED fluorimeter Quantalase LF-2a, the uranium concentration of the groundwater in the Ganderbal and Budgam districts of Jammu and Kashmir was assessed at 30 distinct places. It has been determined that the uranium concentrations in the districts of Ganderbal and Budgam vary from 0.17 to 1.36 μgl−1 and 0.04 to 1.71 μgl−1, respectively, with average values of 0.32 and 1.71 μg−1. It is observed that newborns are more radiation-sensitive than adults. The epidermis, thyroid gland, red bone marrow, and bones receive the maximum radiation doses. As a result, these organs are more vulnerable to uranium's radioactive effects. In the current investigation, every sample has been found to fall within the 30 μgl−1 allowable limits established by the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO).
Optimized wastewater management utilizing multivariate statistical analysis: a case study of the Mascara wastewater treatment plant, Algeria
Authors: Benstaali Imène , Talia Amel , Benadela Laouni 
Published: August 2024

ABSTRACT
Effective wastewater management is crucial in regions experiencing water scarcity and environmental stressors, such as pollution and climate change. Optimizing treatment processes is essential for achieving environmental sustainability. This study aims to highlight the importance of effective wastewater management strategies, particularly in regions facing water scarcity. Our objective was to identify key factors influencing the treatment process. Therefore, we evaluated associations between physicochemical parameters using multivariate statistical methods, including Principal Component Analysis (PCA) and Hierarchical Ascendant Classification (HAC). Our findings categorize the monthly water samples into three distinct groups based on levels of organic pollution: the first group (July, August, and September) is characterized by high oxygenation levels and significantly low organic pollution, indicating optimal system operation. The second group (April, October, November, and December) exhibits low oxygenation and low organic pollution, promoting sludge settling and pollutant reduction. The third group (January, February, March, May, and June) shows significantly high organic pollution and low oxygenation, which corresponds to unfavorable environmental conditions. Our study demonstrates the effectiveness of multivariate statistical methods in optimizing wastewater treatment processes, providing crucial insights for environmental sustainability and water resource management.
Fuzzy logic-based prediction and parametric optimizing using particle swarm optimization for performance improvement in pyramid solar still
Authors: Senthilkumar N. , Yuvaperiyasamy M. , Deepanraj B. , Sabari K. 
Published: August 2024

ABSTRACT

The primary objective of this study is to develop a robust model that employs a fuzzy logic interface (FL) and particle swarm optimization (PSO) to forecast the optimal parameters of a pyramid solar still (PSS). The model considers a range of environmental variables and varying levels of silver nanoparticles (Ag) mixed with paraffin wax, serving as a phase change material (PCM). The study focuses on three key factors: solar intensity ranging from 350 to 950 W/m2, water depth varying between 4 and 8 cm, and silver (Ag) nanoparticle concentration ranging from 0.5 to 1.5% and corresponding output responses are productivity (P), glass temperature (Tg), and basin water temperature (Tw). The experimental design is based on Taguchi's L9 orthogonal array. A technique for ordering preference by similarity to the ideal solution (TOPSIS) is utilized to optimize the process parameters of PSS. Incorporating a fuzzy inference system (FIS) aims to minimize the uncertainty within the system, and the particle swarm optimization algorithm is employed to fine-tune the optimal settings. These methodologies are employed to forecast the optimal conditions required to enhance the productivity of the PSS.