MethodsXPub Date : 2025-05-16DOI: 10.1016/j.mex.2025.103374
Karwan M. Hama Rawf
{"title":"KuSL2023: A standard for Kurdish sign language detection and classification using hand tracking and machine learning","authors":"Karwan M. Hama Rawf","doi":"10.1016/j.mex.2025.103374","DOIUrl":"10.1016/j.mex.2025.103374","url":null,"abstract":"<div><div>Sign Language Recognition (SLR) plays a vital role in enhancing communication for the deaf and hearing-impaired communities, yet there has been a lack of resources for Kurdish Sign Language (KuSL). To address this, a comprehensive standard for KuSL detection and classification has been introduced. This standard includes the creation of a real-time KuSL recognition dataset, focusing on hand shape classification, composed of 71,400 images derived from merging and refining two key datasets: ASL and ArSL2018. The ArSL2018 dataset, aligned with the Kurdish script, contributed 54,049 images, while the ASL dataset added 78,000 RGB images, representing 34 Kurdish sign categories and capturing a variety of lighting conditions, angles, and backgrounds. Various machine learning models were employed to evaluate system performance. The CNN model achieved an accuracy of 98.22 %, while traditional classifiers such as KNN and LightGBM reached 95.98 % and 96.94 %, respectively, with considerably faster training times. These findings underscore the robustness of the KuSL dataset, which not only delivers high accuracy and efficiency but also sets a new benchmark for advancing Kurdish Sign Language recognition and broader gesture recognition technology.<ul><li><span>•</span><span><div>Provides the first standardized dataset for Kurdish Sign Language recognition using 71,400 annotated images.</div></span></li><li><span>•</span><span><div>Demonstrates high classification accuracy using CNN (98.22 %) and traditional models like KNN and LightGBM.</div></span></li><li><span>•</span><span><div>Enables real-time hand sign recognition and supports the development of assistive technologies for the deaf community.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103374"},"PeriodicalIF":1.6,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MethodsXPub Date : 2025-05-14DOI: 10.1016/j.mex.2025.103348
Suresh Kumar Samarla , Maragathavalli P
{"title":"An anatomically enhanced and clinically validated framework for lung abnormality classification using deep features and KL divergence","authors":"Suresh Kumar Samarla , Maragathavalli P","doi":"10.1016/j.mex.2025.103348","DOIUrl":"10.1016/j.mex.2025.103348","url":null,"abstract":"<div><div>Detecting lung abnormalities via chest X-rays is challenging due to understated tissue variations often ignored by traditional methods. Augmentation techniques like rotation or flipping risk distorting critical anatomical features, actually leading to misdiagnosis. This paper proposes a novel two-stage ASCE (Anatomical Segmentation and Color-Based Enhancement) framework for precise and efficient classification of lung abnormalities while preserving anatomical integrity.</div><div>Stage 1 classifies Normal vs. Pneumonia with 95 % accuracy, an AUC of 0.98, and an F1-score of 0.92. Stage 2 distinguishes Pneumonia into Viral and Bacterial subtypes with 100 % accuracy and F1-score. This approach integrates segmentation and tissue-specific color enhancements with Kullback-Leibler (KL) divergence, quantifying deviations from healthy lung regions for improved classification. The lightweight pipeline ensures computational efficiency (∼0.06s/image) and clinical interpretability by preserving diagnostic features, enhancing visibility, and enabling quantitative analysis.<ul><li><span>1.</span><span><div><strong>Preserving Anatomical Structures:</strong> The methodology ensures that diagnostic features are preserved and highlighted with Anatomy-Preserved Segmentation</div></span></li><li><span>2.</span><span><div><strong>Enhancing Diagnostic Visibility:</strong> The system employs targeted colour-based enhancement that improves the visibility of potential abnormalities</div></span></li><li><span>3.</span><span><div><strong>Quantitative Analysis with Kullback-Leibler (KL) divergence:</strong> The model enhances precise identification of abnormal tissue by comparing the probability distributions of healthy lungs and abnormal areas</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103348"},"PeriodicalIF":1.6,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MethodsXPub Date : 2025-05-12DOI: 10.1016/j.mex.2025.103362
Sayyed Johar , G.R. Manjula
{"title":"PhysioDimClassifier—imbalance data classifier model for IoMT-based remote patient monitoring systems","authors":"Sayyed Johar , G.R. Manjula","doi":"10.1016/j.mex.2025.103362","DOIUrl":"10.1016/j.mex.2025.103362","url":null,"abstract":"<div><div>Remote patient monitoring systems (RPMS) using the Internet of Medical Things (IoMT) continuously collect and exchange periodic sensor-observations through communication modules. However, these data streams often contain relevant and irrelevant series, leading to imbalance issues in physiological disease assessment. This research introduces a PhysioDimClassifier (PDC), a novel model to detect and mitigate imbalanced data in physiological disease diagnosis. The proposed model identifies the likenesses and permanence within observation sequences, classifying them as normal or imbalanced based on monitoring duration and sensor communication time. A rotational tree classifier trackspermanence sequences, ensuring accurate classification of imbalanced data. By analyzingsequence interruptions, the model improves the retention of imbalanced data patterns, reducing misclassification. Experimental validation demonstrates that PDCM enhances data accuracy by up to 12.61 %, improves imbalance data detection by 13.23 %, increases classification rate by 10.98 %, lowers data imbalance by 11.22 %, and decreases assessment time by 10.5 %. These improvements contribute to timely and accurate physiological disease diagnosis in IoMT-based RPMS, optimizing clinical decision-making and patient outcomes. The proposed approach providesa robust, scalable, and efficient solution for handling imbalanced physiological data in real-time healthcare applications.<ul><li><span>•</span><span><div>Introduces PhysioDimClassifier (PDC), a novel model to detect and mitigate imbalanced physiological data.</div></span></li><li><span>•</span><span><div>Employing a rotational tree classifier for sequence performance tracking and imbalance classification.</div></span></li><li><span>•</span><span><div>Enhances classification accuracy and reduces imbalance effects, ensuring improved disease diagnosis in IoMT-based RPMS.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103362"},"PeriodicalIF":1.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MethodsXPub Date : 2025-05-12DOI: 10.1016/j.mex.2025.103369
Víctor Eduardo Rojas-Pérez , Eduardo Parra Villegas
{"title":"Dataset and analysis of genetic variability of Severe Acute Respiratory Syndrome Coronavirus 2 from different areas of the respiratory tract","authors":"Víctor Eduardo Rojas-Pérez , Eduardo Parra Villegas","doi":"10.1016/j.mex.2025.103369","DOIUrl":"10.1016/j.mex.2025.103369","url":null,"abstract":"<div><div>Shannon entropy data were obtained from whole genome sequences of SARS-CoV-2 virus from COVID-19 positive patients from nasopharyngeal and tracheal aspirate samples, these sequences were downloaded from the NCBI public database. The study included 57 genomic sequences of the SARS-CoV-2 virus. A total of 57 samples (<em>n</em> = 30 oronasopharyngeal and <em>n</em> = 27 tracheal aspirates) underwent bioinformatics-based analysis with the objective of determining the extent of viral variability. These sequences were used to perform a genetic variability analysis of the virus with the H(x) function of the BioEdit program and using the Wuhan-Hu-1 reference genome. The generated Shannon entropy data were filtered to comparatively determine the magnitudes of genetic variability between sequences from nasopharyngeal and tracheal aspirate samples using an R script.<ul><li><span>•</span><span><div>This paper proposes a workflow for the bionformatic analysis of SARS-CoV-2 genetic variation from different respiratory system samples.</div></span></li><li><span>•</span><span><div>This bioinformatics workflow could help to differentiate changes in the evolution of the viral population in different compartments of the respiratory system.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103369"},"PeriodicalIF":1.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A streamlined tandem affinity purification of His-MBP-SpyCas9, without buffer exchange, suitable for in vitro cleavage applications","authors":"Filippo Fronza , Roberto Verardo , Claudio Schneider","doi":"10.1016/j.mex.2025.103368","DOIUrl":"10.1016/j.mex.2025.103368","url":null,"abstract":"<div><div>We present a streamlined protocol for the purification of recombinant doubly-tagged His-MBP-SpyCas9 protein utilizing a dual affinity chromatography approach whereby the elution volume from immobilized metal ion affinity chromatography (IMAC) is loaded directly onto maltose-binding protein (MBP) affinity chromatography. This protocol, by optimizing the buffer composition throughout the process, eliminates the need for buffer exchanges thereby reducing the risk of protein precipitation. The purification process, from bacterial harvest to final product, can be completed within a single working day. The purified Cas9 protein is suitable for <em>in vitro</em> cleavage assays as validated using sgRNA/complementary dsDNA-reporter oligonucleotides. In fact, the elution buffer from the MBP binding column is suitable for storage and the purified protein can be stored/aliquoted at -20 °C after the addition of glycerol, to be used directly <em>in vitro</em> cleavage assays without additional processing.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103368"},"PeriodicalIF":1.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MethodsXPub Date : 2025-05-10DOI: 10.1016/j.mex.2025.103363
M. Fereydani , A. Jalalian , N. Saber
{"title":"Green synthesis of silver nanoparticles from Cuscuta epithymum extract, evaluation of antibacterial, antioxidant activity, cytotoxic effect on MCF-7 cell line","authors":"M. Fereydani , A. Jalalian , N. Saber","doi":"10.1016/j.mex.2025.103363","DOIUrl":"10.1016/j.mex.2025.103363","url":null,"abstract":"<div><div>Using plants for the green synthesis of nanoparticles is a cost-effective, non-toxic, and environmentally friendly method. This study synthesized silver nanoparticles using <em>Cuscuta epithymum</em> extract, and their biological activities were evaluated. The <em>C. epithymum</em> extract was prepared by maceration and the synthesis of AgNPs using a green method. Confirmation of AgNPs formation was achieved through UV-Vis and the absorption peak was observed at 425 nm, and their morphology and functional groups were determined by FESEM, TEM, XRD, and FT-IR. The nanoparticles were spherical with a size of 15–60 nm. The antioxidant activity of AgNPs was calculated using the DPPH assay (IC<sub>50</sub>=45.55 mg/L), and antibacterial properties were obtained by Disk Diffusion methods showed the AgNPs had strong antimicrobial activity. MTT assay showed that the AgNPs caused cytotoxicity in MCF-7 with an IC<sub>50</sub>=42.53 mg/L, 36.78 mg/L, and 26.86 mg/L (<em>P</em> < 0.0001) after 12, 24, and 48 respectively. It can be concluded that <em>C. epithymum</em> extract can reduce Ag+ ions to silver nanoparticles, which possess excellent antioxidant, antibacterial, and anti-tumor characteristics.<ul><li><span>•</span><span><div><em>C. epitymum</em> extract could regenerate Ag ions and synthesize silver nanoparticles.</div></span></li><li><span>•</span><span><div>Morphologically investigated by XRD, FESEM, TEM, and FT-IR, their results showed spherical nanoparticles with a 15–60 nm particle size.</div></span></li><li><span>•</span><span><div>Silver nanoparticles had significant antioxidant, antibacterial, and cytotoxic properties.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103363"},"PeriodicalIF":1.6,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MethodsXPub Date : 2025-05-07DOI: 10.1016/j.mex.2025.103351
Lucas S. Hollstein , Svenja Groth , Kerstin Schmitt , Oliver Valerius , Stefanie Pöggeler
{"title":"In vivo proximity-labeling with miniTurboID to screen for protein-protein interactions in the filamentous ascomycete Sordaria macrospora","authors":"Lucas S. Hollstein , Svenja Groth , Kerstin Schmitt , Oliver Valerius , Stefanie Pöggeler","doi":"10.1016/j.mex.2025.103351","DOIUrl":"10.1016/j.mex.2025.103351","url":null,"abstract":"<div><div>The <u>Bio</u>tin <u>Id</u>entification (BioID) method applies proximity-dependent labeling of co-localizing proteins to screen for protein-protein interactions <em>in vivo</em>. Therefore, the <u>p</u>rotein <u>o</u>f <u>i</u>nterest (POI) is fused to a promiscuous biotin ligase. This ligase covalently biotinylates proximal proteins, which allows their specific enrichment and subsequent identification by mass spectrometry. In recent research, the BioID method was applied in the filamentous ascomycete <em>Sordaria macrospora</em> using a codon optimized TurboID ligase. In this study, we applied a smaller variant of the TurboID biotin ligase, named <em>mini</em>TurboID to perform BioID experiments in <em>S. macrospora</em>. Here, we provide a comprehensive and detailed guideline of experimental steps for the application of BioID in filamentous fungi.<ul><li><span>•</span><span><div>The BioID method screens for protein-protein interactions via <em>in vivo</em> labeling of nearby proteins through a biotin ligase, which is fused to the POI</div></span></li><li><span>•</span><span><div>TurboID and <em>mini</em>TurboID ligases can be used for BioID experiments in the filamentous fungus <em>Sordaria macrospora</em></div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103351"},"PeriodicalIF":1.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bridging language gaps: The role of NLP and speech recognition in oral english instruction","authors":"Parul Dubey , Pushkar Dubey , Rohit Raja , Sapna Singh Kshatri","doi":"10.1016/j.mex.2025.103359","DOIUrl":"10.1016/j.mex.2025.103359","url":null,"abstract":"<div><div>The Natural Language Processing (NLP) and speech recognition have transformed language learning by providing interactive and real-time feedback, enhancing oral English proficiency. These technologies facilitate personalized and adaptive learning, making pronunciation and fluency improvement more efficient. Traditional methods lack real-time speech assessment and individualized feedback, limiting learners' progress. Existing speech recognition models struggle with diverse accents, variations in speaking styles, and computational efficiency, reducing their effectiveness in real-world applications. This study utilizes three datasets—including a custom dataset of 882 English teachers, the CMU ARCTIC corpus, and LibriSpeech Clean—to ensure generalizability and robustness. The methodology integrates Hidden Markov Models for speech recognition, NLP-based text analysis, and computer vision-based lip movement detection to create an adaptive multimodal learning system. The novelty of this study lies in its real-time Bayesian feedback mechanism and multimodal integration of audio, visual, and textual data, enabling dynamic and personalized oral instruction. Performance is evaluated using recognition accuracy, processing speed, and statistical significance testing. The continuous HMM model achieves up to 97.5 % accuracy and significantly outperforms existing models such as MLP-LSTM and GPT-3.5-turbo (<em>p</em> < 0.05) across all datasets. Developed a multimodal system that combines speech, text, and visual data to enhance real-time oral English learning.<ul><li><span>•</span><span><div>Collected and annotated a diverse dataset of English speech recordings from teachers across various accents and speaking styles.</div></span></li><li><span>•</span><span><div>Designed an adaptive feedback framework to provide learners with immediate, personalized insights into their pronunciation and fluency.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103359"},"PeriodicalIF":1.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Investigating the adsorption of volatile organic compounds from kerosene using clinoptilolite (natural zeolite) modified by cationic surfactant cetyltrimethylammonium bromide","authors":"Motahareh Majidi Trojeni, Abdolraouf Samadi-Maybodi, Haniyeh Shafiei","doi":"10.1016/j.mex.2025.103354","DOIUrl":"10.1016/j.mex.2025.103354","url":null,"abstract":"<div><div>Volatile organic compounds (VOCs) from kerosene pose significant environmental and health risks, necessitating effective remediation strategies. In this work, the natural clinoptilolite zeolite was modified using cationic surfactant cetyltrimethylammonium bromide (CTAB) and it was applied as an adsorbent for adsorption of VOCs from kerosene. The adsorption efficacy (R%) was optimized using Experimental design. The parameters of contact time, pH and adsorbent dosage that can be potentially influence on the adsorption efficiency were considered in the optimization process. Results of the optimization process indicated that the highest of adsorption efficiency was obtained as follows: pH= 3.0, dose of adsorbent = 0.4 g and contact time 180 min. To find the behavior of adsorption, the isotherm models of Freundlich and Langmuir were studied. Also, thermodynamic analysis showed the process was spontaneous and endothermic. Results indicated the adsorption process follows a Langmuir isotherm model with a coefficient of determination (R<sup>2</sup>) of 0.9906. The adsorption of VOCs from kerosene by a cationic surfactant-modified zeolite was higher. This research underscores the potential of surfactant-modified clinoptilolite as a viable and efficient adsorbent for the remediation of VOC emissions from kerosene, paving the way for sustainable environmental practices.<ul><li><span>•</span><span><div>Successfully modified natural clinoptilolite zeolite with cetyltrimethylammonium bromide (CTAB) to enhance VOC adsorption from kerosene.</div></span></li><li><span>•</span><span><div>Characterized the modified zeolite using Fourier transform infrared (FTIR) spectrophotometry and energy dispersive spectroscopy (EDX).</div></span></li><li><span>•</span><span><div>Achieved optimal adsorption efficiency with conditions of pH= 3.0, adsorbent dosage of 0.4 g and contact time of 180 min.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103354"},"PeriodicalIF":1.6,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MethodsXPub Date : 2025-04-30DOI: 10.1016/j.mex.2025.103345
Jennifer J. Hollis , Miguel G. Cruz , W. Lachlan McCaw , James S. Gould , Stephanie A. Samson
{"title":"An efficient and comprehensive field protocol for assessing fuel characteristics for fire behaviour modelling in Australian open forests","authors":"Jennifer J. Hollis , Miguel G. Cruz , W. Lachlan McCaw , James S. Gould , Stephanie A. Samson","doi":"10.1016/j.mex.2025.103345","DOIUrl":"10.1016/j.mex.2025.103345","url":null,"abstract":"<div><div>Knowledge of fuel characteristics and their spatial and temporal distribution is increasingly important as fire managers rely on this information to quantify fire risk, plan prescribed burning activities, forecast fire danger and predict wildland fire behaviour and effects. Current fuel inventory approaches used in Australia largely rely on visual assessment methods that are subjective and lack the consistency and accuracy required for fire management applications.</div><div>We describe a protocol to quantify characteristics for various fuel strata considered in Australian fire modelling applications, namely: litter and suspended dead fuels; downed wood debris; live understorey; bark; and overstorey canopy. The method provides information about:<ul><li><span>•</span><span><div>Cover and height (or depth) of each strata;</div></span></li><li><span>•</span><span><div>Mass of fine fuels of litter, dead suspended and live understorey layers (dead fuel diameter (<em>d</em>) ≤ 0.6 cm, live fuel <em>d</em> ≤ <em>0.4</em> cm); and</div></span></li><li><span>•</span><span><div>Mass and size class distribution of downed woody fuels (<em>d</em>>0.6 cm).</div></span></li></ul></div><div>The protocol integrates a variety of sampling methods including destructive sampling for fine fuel particles, line intersect method for downed woody fuel, and indirect approaches relying on double sampling techniques to estimate live understorey, bark and overstorey canopy fuels. The protocol can be adapted to enable application to situations with distinct accuracy requirements.</div><div>Data collected using the protocol will have direct use in developing models of forest fuel dynamics and evaluating outputs from remote sensing approaches to describe these fuels.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103345"},"PeriodicalIF":1.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}