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英文中文

A novel method to determine background concentrations and spatial distributions of heavy metals in soil at large scale using machine learning coupled with remote sensing-terrain attributes

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-21 DOI: 10.1016/j.mex.2025.103180

Magboul M. Sulieman , Fuat Kaya , Abdullah S. Al-Farraj , Eric C. Brevik

Soil heavy metals are among the most hazardous materials in the environment. Their harmful effects can extend to surrounding systems (air, plants, water), and given the appropriate conditions may ultimately have negative effects on human health. Thus, preventing pollution and protecting pristine soils and preindustrial areas from human activities that lead to the concentration of heavy metals (HMs) is a priority. Here, a novel methodology was proposed to establish background concentrations of eight soil HMs, cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn), and digitally map their spatial distributions in an area (i.e., harrats region) that has not yet been impacted by industrial activity. The proposed methodology combined measurements of the target HMs and fifty-two environmental covariates (ECOVs) derived from 2017 to 2021 Landsat 8/9 OLI and Shuttle Radar Topography Mission (SRTM)-derived terrain attributes. Random forest and stepwise multiple linear regression models were further used to digitally map the studied HMs. The methodology is important for any future environmental pollution/monitoring studies in the area and can be applied in other similar environments. Machine learning algorithms show great ability to use available environmental variables and investigate the relationships between the factors influencing HMs accumulation under a given soil environment. The proposed methodology was effective for describing HMs spatial variability in the environments investigated.

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The proposed method is a novel way to predict soil HMs and their spatial distribution over large areas.
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Remote sensing/digital elevation models (DEMs)-derived ECOVs are useful for predicting and digitally mapping soil HMs, thus important for future environmental monitoring studies.
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Explainable algorithms (i.e., RF and SMLR) are able to utilize ECOVs for HMs prediction and to establish background concentrations over large areas.

Therefore, the combination of machine learning and RS/DEMs-based ECOVs is crucial to overcome the disadvantages of HMs determination via conventional methods.

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引用次数: 0

Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-20 DOI: 10.1016/j.mex.2025.103178

H. S. Gowri Yaamini , Swathi K J , Manohar N , Ajay Kumar G

Accurate and precise detection of lanes and traffic signs is predominant for the safety and efficiency of autonomous vehicles and these two significant tasks should be addressed to handle Indian traffic conditions. There are several state-of-art You Only Live Once (YOLO) models trained on benchmark datasets which fails to cater the challenges of Indian roads. To address these issues, the models need to be trained with a wide variety of Indian data samples for the autonomous vehicles to perform better in India. YOLOv8 algorithm has its challenges but gives better precision results and YOLOv8 nano variant is widely used as it is computationally less complex comparatively. Through rigorous evaluations of diverseness in the datasets, the proposed YOLOv8n transfer learning models exhibits remarkable performance with a mean Average Precision (mAP) of 90.6 % and inference speed of 117 frames per second (fps) for lane detection whereas, a notable mAP of 81.3 % for traffic sign detection model with a processing speed of 56 fps.

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YOLOv8n Transfer Learning approach by adjusting architecture for lane and traffic sign detection in Indian diverse Urban, Suburban, and Highway scenarios.
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Dataset with 22,400 images of normal and complex Indian scenarios include crude weathering of roads, traffic conditions, diverse tropical weather conditions, partially occluded and partially erased lanes, and traffic signs.
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The model performance with notable precision and frame wise inference.

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引用次数: 0

A straightforward Py-GC/MS methodology for quantification of microplastics in tap water

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-17 DOI: 10.1016/j.mex.2025.103173

Alexander Ccanccapa-Cartagena , Anandu Nair Gopakumar , Maryam Salehi

This study introduces a cost-effective and streamlined Pyrolysis Gas Chromatography-Mass Spectrometry (Py-GC/MS) methodology for detecting and quantifying microplastics in tap water, focusing on seven common polymers. Unlike conventional approaches relying on expensive pyrolyzate libraries, this method identifies pyrolysis fragments by matching their m/z values with commercially available mass spectral libraries (Wiley Registry 12th Edition/NIST 2020) and confirms findings using pure polymer standards. Recovery was evaluated using two approaches, demonstrating that analysis of the entire filter provided more accurate results compared to extrapolation from subsections. The method exhibited excellent linearity for all targeted polymers (R² > 0.996) and achieved detection limits as low as 0.01 µg for polystyrene (PS) and up to 2.59 µg for polyethylene (PE). Application to tap water samples revealed consistent detection of PS, ranging from 2.532 to 2.571 ng/L in morning samples and 0.867 to 1.540 ng/L in afternoon samples, with polypropylene and PE below the limit of quantification (<LOQ). This method provides a reliable, efficient, and cost-effective tool for routine laboratory analysis of microplastics in tap water and other environmental matrices.

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A 23-minute Py-GC/MS method efficiently quantifies microplastics in tap water.
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Cost-effective strategy using commercially available mass spectral libraries.
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Accurate quantification with ng/L sensitivity validated by pure polymer standards.

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引用次数: 0

Enhancing network lifetime in WSNs through coot algorithm-based energy management model

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-16 DOI: 10.1016/j.mex.2025.103176

Namita Shinde, Dr. Vinod H․ Patil

To improve the performance of Wireless Sensor Networks (WSN), this study offers a novel energy-efficient clustering and routing technique based on the Coot Optimization Algorithm (COA). This addresses issues such as high energy consumption, communication delays, and security.

To ensure energy savings and network reliability, the fitness function evaluates cluster heads and best routes based on constraints.

COOT outperforms other Metaheuristics Algorithms like Butterfly Optimization Algorithm, Genetic Algorithm, Tunicate Swarm Gray Wolf Optimization Algorithm, and Bird Swarm Algorithm in simulation with performance measurements and enhancing network functionality and protection.

Key methodology points include:

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Proposed a multiple constraints clustering and routing technique using COAto solve the most crucial issues that arise in WSNs.
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Integrated an advanced fitness function that determines cluster head selection, and the routing path based on residual energy, delay, security, trust, distance, and link quality so that energy load is evenly distributed and credible data flow is maintained across the network and made Innovative and Effective Solution.
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Proven Results Demonstrated superior network performance, achieving the lowest delay, highest network lifetime (3571 rounds) and enhanced security (0.8) and trust (0.6) compared to existing algorithms with less energy consumption, making it the most suitable solution for WSN performance improvement.

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引用次数: 0

Development and validation of a protocol to determine product perception in relation to the moment of the day

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-16 DOI: 10.1016/j.mex.2025.103174

M. Visalli , S. Plano , C. Tortorello , D. Vigo , M.V. Galmarini

Chronotype refers to an individual's tendency to engage in activities either earlier or later, in alignment with the biological rhythm of their body and its interaction with the environmental cycle. Chronotypes influence food preferences and meal timing, yet most studies rely solely on questionnaires without integrating real-time tasting data. To address this gap, we developed and validated a method to measure sensory perception and examine its variations throughout the day in alignment with circadian rhythms. Fifty-two university students completed the Munich Chronotype Questionnaire and, over four days within a week, they participated in sensory evaluations using a web-based questionnaire. At four daily time slots (morning, midday, afternoon, evening), participants tasted candies and assessed some sensory attributes—sweetness, sourness, bitterness, freshness, and overall flavor—using the Rate-All-That-Apply method. Before each evaluation, they also reported their level of hunger, thirst, tiredness, and willingness to complete the task. Reminders were sent via pre-programmed messages to ensure adherence to the schedule. The results demonstrate the feasibility of the method, with low attrition rates and consistent participant motivation over the study period. Sensory perception was found to vary across the day and in relation to chronotype, highlighting the method's potential for advancing research in sensory chrononutrition.

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A web-based questionnaire including tasting was developed to assess sensory perception at different times of the day over four days.
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Perception was analyzed in relation to chronotype.
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Face validity was confirmed, as significant variations based on chronotypes were observed.

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引用次数: 0

Quantitative assessment of brain metabolism in mice using non-contrast MRI at 11.7T

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-16 DOI: 10.1016/j.mex.2025.103175

Xiuli Yang, Yuguo Li, Hanzhang Lu, Zhiliang Wei

Brain oxygen metabolism indicates the rate of energy consumption and is a potential marker of pathological changes. Positron emission tomography (PET) is the gold standard for measuring metabolic rates using radioactive tracers. However, its application in preclinical studies, particularly with rodent animals, is constrained by the need for arterial input function measurements and on-site cyclotron facilities for tracer preparation. As an alternative, non-invasive, non-contrast MRI techniques, such as T2-relaxation-under-spin-tagging (TRUST) and phase-contrast (PC) MRI, can be used for evaluating brain metabolism in vivo. This study outlines a step-by-step method for implementing TRUST and PC MRI in mice at 11.7T scanner. The proposed method yields non-invasive, non-contrast quantitative measurements of global brain metabolism in approximately 20 min, paving the way for broader applications in future pathophysiological studies.

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Non-invasive and non-contrast assessment of brain metabolism in mice.
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Quantitative measurement of metabolic rate in approximately 20 min.

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引用次数: 0

A method for siRNA-mediated knockdown of target genes in RA-induced neurogenesis using P19 cells

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-16 DOI: 10.1016/j.mex.2025.103177

Hossein Khodadadi , Hiroaki Taniguchi

This study presents a comprehensive protocol for siRNA-mediated knockdown in the differentiation of P19 cells into neuronal-like cells. Utilizing a retinoic acid (RA)-induced neurogenesis model, P19 cells were cultured under specific conditions that facilitated the formation of embryoid bodies (EBs), which were subsequently differentiated into neuronal-like cells. In this investigation, we specifically targeted the Nfe2l1 gene using siRNA transfection to assess the efficiency and effectiveness of our protocol throughout the neuronal differentiation process. Validation of the differentiation was performed through quantitative reverse transcription PCR (RT-qPCR) analysis, measuring the expression levels of key neuronal markers, including Map2 and Pax6 along with the pluripotency marker Oct4. Additionally, the efficiency of the siRNA-mediated knockdown was confirmed by western blot analysis, which demonstrated significant gene silencing at protein levels. These findings underscore the potential of siRNA technology in elucidating gene function during neuronal differentiation and highlight the critical role of targeted gene silencing in advancing neurogenesis research. Furthermore, this study provides a robust and reliable protocol for gene knockdown in neuronal-like cells derived from P19 cells, thereby facilitating further investigations into the intricate molecular mechanisms that govern neurogenesis, neuronal maturation, and overall brain development.

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Developed a novel protocol for targeted gene knockdown in P19 cells during neuronal differentiation.
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Successful silencing of the Nfe2l1 gene during neuronal differentiation, validated by western blot.
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This study provides a reliable protocol for gene knockdown in neuronal differentiation, aiding functional studies of genes in neurogenesis.

{"title":"A method for siRNA-mediated knockdown of target genes in RA-induced neurogenesis using P19 cells","authors":"Hossein Khodadadi , Hiroaki Taniguchi","doi":"10.1016/j.mex.2025.103177","DOIUrl":"10.1016/j.mex.2025.103177","url":null,"abstract":"<div><div>This study presents a comprehensive protocol for siRNA-mediated knockdown in the differentiation of P19 cells into neuronal-like cells. Utilizing a retinoic acid (RA)-induced neurogenesis model, P19 cells were cultured under specific conditions that facilitated the formation of embryoid bodies (EBs), which were subsequently differentiated into neuronal-like cells. In this investigation, we specifically targeted the Nfe2l1 gene using siRNA transfection to assess the efficiency and effectiveness of our protocol throughout the neuronal differentiation process. Validation of the differentiation was performed through quantitative reverse transcription PCR (RT-qPCR) analysis, measuring the expression levels of key neuronal markers, including Map2 and Pax6 along with the pluripotency marker Oct4. Additionally, the efficiency of the siRNA-mediated knockdown was confirmed by western blot analysis, which demonstrated significant gene silencing at protein levels. These findings underscore the potential of siRNA technology in elucidating gene function during neuronal differentiation and highlight the critical role of targeted gene silencing in advancing neurogenesis research. Furthermore, this study provides a robust and reliable protocol for gene knockdown in neuronal-like cells derived from P19 cells, thereby facilitating further investigations into the intricate molecular mechanisms that govern neurogenesis, neuronal maturation, and overall brain development.<ul><li><span>•</span><span><div>Developed a novel protocol for targeted gene knockdown in P19 cells during neuronal differentiation.</div></span></li><li><span>•</span><span><div>Successful silencing of the Nfe2l1 gene during neuronal differentiation, validated by western blot.</div></span></li><li><span>•</span><span><div>This study provides a reliable protocol for gene knockdown in neuronal differentiation, aiding functional studies of genes in neurogenesis.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103177"},"PeriodicalIF":1.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-enhanced corn disease diagnosis using few-shot learning and VGG16

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-15 DOI: 10.1016/j.mex.2025.103172

Ruchi Rani , Jayakrushna Sahoo , Sivaiah Bellamkonda , Sumit Kumar

Plant Disease Detection in the early stage is paramount. Traditionally, it was done manually by the farmers, which is a laborious and time-intensive task. With the advent of AI, Machine Learning and Deep Learning methods are used to detect and categorize plant diseases. However, they rely on extensive datasets for accurate prediction, which is impracticable to acquire and annotate. Thus, Few Shot Learning is the state-of-the-art model in machine learning, which requires minimum examples to train the model for generalization. As humans need a few examples to recognize things, Few-shot Learning mimics the same human brain process. The proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset. An attention module is integrated with the backbone, and further, prototypical few-shot learning is used for corn disease prediction and classification with an accuracy of 98.25 %.

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The proposed model intends to identify the diseases early, so the insights generated would be relevant for farmers, and probable losses can be reduced.
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By applying Few-Shot Learning, the system avoids the significant challenges of requiring extensively annotated datasets, making it feasible for real-world agricultural applications.
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Incorporating a fine-tuned VGG16 backbone along with an attention mechanism and prototypical Few-Shot Learning results in a robust and scalable solution with high accuracy for classifying corn diseases.

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引用次数: 0

On derived t-path, t=2,3 signed graph and t-distance signed graph

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-14 DOI: 10.1016/j.mex.2025.103160

Deepa Sinha, Sachin Somra

<div><div>A signed graph <span><math><mstyle><mi>Σ</mi></mstyle></math></span> is a pair <span><math><mrow><mstyle><mi>Σ</mi></mstyle><mo>=</mo><mo>(</mo><mrow><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mi>u</mi></msup><mo>,</mo><mi>σ</mi></mrow><mo>)</mo><mspace></mspace></mrow></math></span>that consists of a graph <span><math><mrow><mo>(</mo><mrow><msup><mrow><mstyle><mi>Σ</mi></mstyle></mrow><mi>u</mi></msup><mo>,</mo><mi>E</mi></mrow><mo>)</mo></mrow></math></span> and a sign mapping called signature <span><math><mi>σ</mi></math></span> from <em>E</em> to the sign group <span><math><mrow><mo>{</mo><mrow><mo>+</mo><mo>,</mo><mo>−</mo></mrow><mo>}</mo></mrow></math></span>. In this paper, we discuss the <em>t</em>-path product signed graph <span><math><mrow><msub><mover><mrow><mo>(</mo><mstyle><mi>Σ</mi></mstyle><mo>)</mo></mrow><mo>^</mo></mover><mi>t</mi></msub><mspace></mspace></mrow></math></span>where vertex set of <span><math><msub><mover><mrow><mo>(</mo><mstyle><mi>Σ</mi></mstyle><mo>)</mo></mrow><mo>^</mo></mover><mi>t</mi></msub></math></span> is the same as that of <span><math><mstyle><mi>Σ</mi></mstyle></math></span> and two vertices are adjacent if there is a path of length <em>t</em>, between them in the signed graph <span><math><mstyle><mi>Σ</mi></mstyle></math></span>. The sign of an edge in the <em>t</em>-path product signed graph is determined by the product of marks of the vertices in the signed graph <span><math><mstyle><mi>Σ</mi></mstyle></math></span>, where the mark of a vertex is the product of signs of all edges incident to it. In this paper, we provide a characterization of <span><math><mstyle><mi>Σ</mi></mstyle></math></span> which are switching equivalent to <em>t</em>-path product signed graphs <span><math><msub><mover><mrow><mo>(</mo><mstyle><mi>Σ</mi></mstyle><mo>)</mo></mrow><mo>^</mo></mover><mi>t</mi></msub></math></span> for <span><math><mrow><mi>t</mi><mo>=</mo><mn>2</mn><mo>,</mo><mn>3</mn></mrow></math></span> which are switching equivalent to <span><math><mstyle><mi>Σ</mi></mstyle></math></span> and also the negation of the signed graph ŋ<span><math><mrow><mo>(</mo><mstyle><mi>Σ</mi></mstyle><mo>)</mo></mrow></math></span> that are switching equivalent to <span><math><msub><mover><mrow><mo>(</mo><mstyle><mi>Σ</mi></mstyle><mo>)</mo></mrow><mo>^</mo></mover><mi>t</mi></msub></math></span> for <span><math><mrow><mi>t</mi><mo>=</mo><mn>2</mn><mo>,</mo><mn>3</mn></mrow></math></span>. We also characterize signed graphs that are switching equivalent to <span><math><mi>t</mi></math></span>-distance signed graph <span><math><msub><mrow><mo>(</mo><mover><mstyle><mi>Σ</mi></mstyle><mo>¯</mo></mover><mo>)</mo></mrow><mi>t</mi></msub></math></span> for <span><math><mrow><mi>t</mi><mo>=</mo><mn>2</mn></mrow></math></span> where 2-distance signed graph <span><math><mrow><msub><mrow><mo>(</mo><mover><mstyle><mi>Σ</mi></mstyle><mo>¯</mo></mover><mo>)</mo></mrow><mn>2</mn></msub><mo>=</mo><mrow><mo>(</mo><mrow><msup><

引用次数: 0

Fast screening of gold in rock samples using polyurethane foam extraction and inductively coupled plasma mass spectrometry determination

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX

Pub Date : 2025-01-14 DOI: 10.1016/j.mex.2025.103170

Jalal Hassan , Naeemeh Zari , Mohammad-Hadi Karbasi

In this work, the volume of sample solution and concentration of gold was optimized for extraction with foam in dimensions (1 × 1 × 1) and then was used for extraction from soil samples. The results showed that the proposed technique has a good analytical efficiency compared to the standard fire assay method and the accuracy of work is in the range of 74–125 %. The equation of linear calibration curve was obtained with regression coefficient better than 0.9997, and the detection and quantification limit of the gold in aqueous and soil sample obtained 0.25 and 0.8 µg kg ⁻¹, respectively.

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This method is inexpensive and fast for determination of gold in various samples.
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This method has high thought of sample determination.
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This method is green chemistry method.

引用次数: 0

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