MethodsX最新文献

{"title":"Simplified method for purifying full-length major type IV pilins: PilA from Acidithiobacillus thiooxidans","authors":"Edgar D. Páez-Pérez,&nbsp;Araceli Hernández-Sánchez,&nbsp;Elvia Alfaro-Saldaña,&nbsp;J. Viridiana García-Meza","doi":"10.1016/j.mex.2025.103520","DOIUrl":"10.1016/j.mex.2025.103520","url":null,"abstract":"<div><div>Type IV major pilins are membrane-associated proteins that play critical roles in bacterial adaptation and survival across diverse environmental conditions, including potentially extreme ones. However, their expression and purification remain challenging due to their hydrophobic nature and tendency to aggregate. Here, we present a cost-effective method for the production and purification of recombinant full-length PilA, the major type IV pilin from the acidophilic bacterium <em>Acidithiobacillus thiooxidans</em>. By expressing the protein as a thioredoxin (Trx) fusion in <em>Escherichia coli</em> and using detergent-based solubilization combined with manual nickel affinity chromatography and spin-column anion exchange, we obtained stable and well folded protein suitable for downstream biophysical assays. Our approach eliminates the need for sophisticated FPLC systems and high-end chromatography columns, making it accessible to laboratories with limited resources. Structural stability of the purified protein was validated through intrinsic fluorescence spectroscopy under varying pH and denaturing conditions. This method can be readily adapted for the production of pilins from extremophilic and other pathogenic bacteria, providing a valuable tool for biotechnological and biomedical applications.<ul><li><span>•</span><span><div>Enables purification of full-length pilins without requiring FPLC or high-cost columns</div></span></li><li><span>•</span><span><div>Applicable to the expression and analysis of structurally challenging pilins</div></span></li><li><span>•</span><span><div>Produces material suitable for biophysical studies, including fluorescence-based assays and structural analyses</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103520"},"PeriodicalIF":1.6,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711926","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":"Deep learning approach with ConvNeXt-SE-attn model for in vitro oral squamous cell carcinoma and chemotherapy analysis","authors":"Abhay Nath ,&nbsp;Om Roy ,&nbsp;Priyanka Silveri ,&nbsp;Sanskruti Patel","doi":"10.1016/j.mex.2025.103519","DOIUrl":"10.1016/j.mex.2025.103519","url":null,"abstract":"<div><div>Oral squamous cell carcinoma (OSCC) continues to present a major worldwide healthcare problem because patients have poor survival outcomes alongside frequent disease returns. Globocan predicts that, OSCC will result in 389,846 new cases and 188,438 deaths globally during 2022 while maintaining an extremely poor 5-year survival rate at about 50%. Our method applies residual connections with Squeeze-and-Excitation blocks along with hybrid attention systems and enhanced activation functions and optimization algorithms to boost gradient movement throughout feature extraction. Compared against established conventional CNN backbones (VGG16, ResNet50, DenseNet121, and more), the proposed ConvNeXt-SE-Attn model outperformed them in all aspects of discrimination and calibration, including precision 97.88% (vs. ≤94.2%), sensitivity 96.82% (vs. ≤92.5%), specificity 95.94% (vs. ≤93.1%), F1 score 97.31% (vs. ≤93.8%), AUC 0.9644 (vs. ≤0.945), and MCC 0.9397 (vs. ≤0.910). The findings are critical to the increased feature-representation power and the robustness of classification of the architecture.</div><div>The proposed architecture employs ConvNeXt backbone with SE blocks and hybrid attention to extract essential details within class boundaries which standard models usually miss.</div><div>The activation through Gaussian-based GReLU incorporates Swish activation together with DropPath regularization for producing smooth gradient patterns which lead to generalizable features across imbalanced datasets.</div><div>Grad-CAM enhances interpretability by showing which image sections lead to predictions in order to enable clinical decisions.</div><div>The model demonstrates its capability as an effective detection method for minimal variations in oral cells which supports precise non-invasive treatment approaches for OSCC.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103519"},"PeriodicalIF":1.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696758","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":"Urea water bath: A novel treatment for monogenean parasite infections in hagfish (Agnatha: Myxiniformes: Myxinidae)","authors":"Taketeru Tomita ,&nbsp;Hiroko Takaoka ,&nbsp;Nozomi Hanahara","doi":"10.1016/j.mex.2025.103518","DOIUrl":"10.1016/j.mex.2025.103518","url":null,"abstract":"<div><div>Monogenean flatworms are pathogenic parasites that infect the skin of marine fish and pose a major problem in aquaculture and aquarium industries. Freshwater baths are the most commonly used method to treat these infections. However, this method is not suitable for certain fish taxa, including hagfish (Agnatha: Myxiniformes: Myxinidae), which have low tolerance to hypoosmotic environments.</div><div>In the present study, we developed a treatment fluid specifically for use in hagfish. The fluid was prepared by dissolving urea in a mixture of chlorine-free tap water and seawater. A 5-min bath of three captive purple hagfish in the fluid successfully eliminated the parasites, with no apparent side effects. To date, this is the only known effective treatment for monogenean infections in hagfish.</div><div><ul><li><span>•</span><span><div>In this study, we developed a method to treat monogenean infections in hagfish.</div></span></li><li><span>•</span><span><div>This method uses a fluid containing a high concentration of urea.</div></span></li><li><span>•</span><span><div>Bathing infected hagfish in the fluid successfully eliminated the parasites without apparent side effects.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103518"},"PeriodicalIF":1.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711929","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":"Bayesian MCMC with Gibbs sampling for saturation flow rate estimation in heterogeneous traffic at pretimed signalized intersections","authors":"Lulusi Lulusi ,&nbsp;Sugiarto Sugiarto ,&nbsp;Sofyan M. Saleh ,&nbsp;Muhammad Isya ,&nbsp;Muhammad Rusdi ,&nbsp;Roudhia Rahma","doi":"10.1016/j.mex.2025.103507","DOIUrl":"10.1016/j.mex.2025.103507","url":null,"abstract":"<div><div>Pretimed signalized intersections significantly contribute to traffic congestion, especially under the heterogeneous traffic conditions commonly observed in emerging economies such as Indonesia. Accurate estimation of the base saturation flow rate (BSFR) is essential for reliable capacity assessment, which influences effective intersection design and operation. However, the current BSFR estimation methods outlined in the Indonesian Highway Capacity Guidelines (IHCG, 2023) rely on outdated linear models derived from the Indonesian Highway Capacity Manual (IHCM, 1997), which are inadequate for addressing contemporary heterogeneous traffic complexities. This study introduces a Bayesian Markov Chain Monte Carlo (MCMC) model employing Gibbs sampling to improve BSFR estimation accuracy. The Bayesian MCMC model achieved a Root Mean Square Error Approximation (RMSEA) of 8.638 % compared to the existing IHCG method, which produced an RMSEA of up to 51.428 %, enabling a more precise intersection capacity design. Additionally, the developed model reduced the BSFR overestimation associated with the IHCG method by approximately 42.79 %, highlighting the potential of Bayesian MCMC methods to effectively address heterogeneous traffic challenges, enhance traffic management strategies, and optimize intersection operations.</div><div>The Bayesian approach provides a probabilistic framework for quantifying uncertainty, allows for the incorporation of prior knowledge to enhance parameter estimation flexibility, and effectively mitigates model overfitting.</div><div>The developed model demonstrates robust statistical validity, characterized by a mean beta parameter value of 403.30, standard deviation of 8.66, and Monte Carlo Standard Error (MCSE) of 0.0008, confirming high reliability and predictive precision.</div><div>The proposed BSFR model exhibited superior performance in fitting empirical data, as evidenced by an RMSE of 240.403 PCU/g/h/We and RMSEA of 8.638 %, indicating an excellent model fit within acceptable thresholds (&lt;10 %).</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103507"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696759","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":"Shewhart type simultaneous univariate control chart based on ranked set sampling scheme","authors":"Sukma Adi Perdana ,&nbsp;Muhammad Mashuri ,&nbsp;Muhammad Ahsan","doi":"10.1016/j.mex.2025.103513","DOIUrl":"10.1016/j.mex.2025.103513","url":null,"abstract":"<div><div>The control charts with the Ranked Set Sampling (RSS) scheme performs better in detecting shifts in mean and variance of the process compared to the control charts with the Simple Random Sampling scheme (SRS). In this article, we develop a Novel Shewhart type simultaneous univariate control chart (Max Chart) based on the RSS scheme. Since the distribution of the statistics <span><math><mrow><mi>M</mi><mo>(</mo><msub><mi>n</mi><mi>i</mi></msub><mo>)</mo></mrow></math></span> based on the RSS scheme is not known, an alternative approach is taken by using an improved bootstrap method in developing a novel Shewhart type simultaneous control chart based on RSS. The results of the Monte Carlo simulation study show that, when compared to the conventional Max chart based on the SRS scheme, the average run length under out-of-control conditions (ARL₁) obtained from that the Improved Bootstrap Max Chart based on the RSS scheme is superior in detecting shifts in mean and variance of the process. A real case example is also given to illustrate the operation and performance of the proposed control chart. Key points:<ul><li><span>1.</span><span><div>A novel Shewhart type simultaneous univariate control chart (Max Chart) based on the RSS scheme is proposed to improve the performance of the of control chart in detecting process shifts.</div></span></li><li><span>2.</span><span><div>The Control limit of Shewhart type simultaneous univariate control chart (Max Chart) based on the RSS scheme was constructed using an Improved Bootstrap method.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103513"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685758","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":"Hybrid genetic algorithms-driven optimization of machine learning models for heart disease prediction","authors":"Sherko H. Murad ,&nbsp;Noor Bahjat Tayfor ,&nbsp;Nozad H. Mahmood ,&nbsp;Lawson Arman","doi":"10.1016/j.mex.2025.103510","DOIUrl":"10.1016/j.mex.2025.103510","url":null,"abstract":"<div><div>Machine learning (ML) models, such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), play a vital role in predicting heart disease. However, their performance is often limited by poor hyperparameter selection. This study presents a novel hybrid approach that uses a Genetic Algorithm (GA) to systematically optimize the hyperparameters of KNN and SVM models, leading to improved classification outcomes. The optimization driven by the GA resulted in significant performance improvements, increasing the classification accuracy of KNN to 95.38 % and SVM to 90 %. Furthermore, there were significant improvements in precision, recall, and F-score. Our findings demonstrate that GA-based hyperparameter tuning is an effective strategy for improving the predictive power and clinical relevance of ML models used for heart disease classification.<ul><li><span>•</span><span><div><strong>GA Driven Optimization:</strong> Genetic algorithm was utilized to fine-tune the hyperparameters of K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers for improved performance.</div></span></li><li><span>•</span><span><div><strong>Significant Performance Gains:</strong> The optimization process aimed to maximize classification across metrics such as accuracy, precision, recall, and F-score.</div></span></li><li><span>•</span><span><div><strong>Improved Accuracy:</strong> The GA-based tuning significantly increased the classification accuracy, improving KNN to 95.38 % and SVM to 90 %.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103510"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685757","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":"Satellite imagery pre-processing and feature extraction for the mapping of coastal ecosystems using Google Earth Engine: A workflow for practitioners","authors":"Ahmad Badruzzaman ,&nbsp;Prawesti Wulandari ,&nbsp;Sainal Sainal ,&nbsp;Matthew Ashley ,&nbsp;Susan Jobling ,&nbsp;Melanie C. Austen ,&nbsp;Radisti A. Praptiwi","doi":"10.1016/j.mex.2025.103516","DOIUrl":"10.1016/j.mex.2025.103516","url":null,"abstract":"<div><div>The use of Google Earth Engine (GEE) is increasingly common in geospatial analysis of satellite images for various environmental management purposes due to its easy accessibility and capabilities to support complex pre-processing and mining of geographic data. In the context of coastal management, GEE provides opportunities for cost-efficient mapping of coastal habitats and their ecosystem service potentials. Understanding the extent of coastal habitats and the spatial and temporal variabilities of their ecosystem services can be useful for management and intervention purposes. GEE is well-suited for this due to its user-friendliness, particularly for non-experts of programming languages, such as area managers and other practitioners. However, there is no specific methodological guideline for the pre-processing and feature extraction of satellite images in GEE that can be readily adopted by these practitioners. This study develops general methodological steps to perform those processes that can be adapted to different management needs. Highlights of this study:<ul><li><span>•</span><span><div>Steps detailed in this method paper will produce processed satellite images readily applicable for machine learning to classify coastal ecosystems.</div></span></li><li><span>•</span><span><div>The development of this adaptable workflow can benefit and empower local area managers, particularly in low-resource settings, to conduct monitoring of their area.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103516"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663266","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":"Methodological approach to assess cardiovascular dynamics in elite cyclists","authors":"Manuel Mateo-March,&nbsp;Alejandro Javaloyes,&nbsp;Iván Peña-González,&nbsp;Manuel Moya-Ramón","doi":"10.1016/j.mex.2025.103515","DOIUrl":"10.1016/j.mex.2025.103515","url":null,"abstract":"<div><div>This study introduces a novel time-series method to optimize performance in professional cycling, analyzing cardiovascular reactivity and power output in elite cyclists during monument races. Integrating power meter and heart rate data, we derive critical power (CP), assess effort intensity (% CP), and track heart rate dynamics across race quartiles (Q1, Q2, Q3, Q4), revealing heart rate dynamics in elite cyclist. Preliminary testing of this method showed that Top 10 cyclists show significantly higher heart rate increase rates in Q1 (<em>p</em> &lt; 0.05) and greater heart rate modulation in Q1 and Q3 (<em>p</em> &lt; 0.05) than non-top cyclists (Top 11–30), indicating superior cardiovascular responsiveness. Key features include:</div><div>• Thorough raw data preprocessing for reliability.</div><div>• Power output normalization to CP for consistent assessment.</div><div>• Sigmoid modeling of heart rate rise to gauge cardiovascular reactivity.</div><div>This R-based, reproducible method empowers sports scientists and coaches to boost cyclist performance, with potential use in other endurance sports.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103515"},"PeriodicalIF":1.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670877","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 spatiotemporal wildfire prediction and decision modeling using transformer networks and fuzzy inference systems","authors":"Parul Dubey ,&nbsp;Pushkar Dubey","doi":"10.1016/j.mex.2025.103498","DOIUrl":"10.1016/j.mex.2025.103498","url":null,"abstract":"<div><div>Wildfires present a growing threat to ecosystems, human settlements, and climate stability, necessitating accurate and interpreted prediction systems. Existing AI-based models often prioritize performance over explainability, limiting their utility in real-time decision-making contexts. Current wildfire forecasting models struggle to incorporate uncertainty and offer transparent response strategies. Moreover, many models fail to integrate domain knowledge in a way that supports actionable interventions. This study utilizes the Canadian Fire Spread Dataset, augmented with Sentinel, ERA5, and SRTM data, encompassing vegetation, meteorological, and topographic variables. The suggested system uses a Transformer-based model to predict fires over time and space, along with a Fuzzy Rule-Based System (FRBS) to create rules for responding to those predictions. This integration allows for both high accuracy and interpretability in decision-making under uncertain environmental conditions. The novelty lies in the use of symbolic fuzzy reasoning layered onto a deep attention-based architecture. Performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The model achieved an F1-score of 92.9 % and accuracy of 94.8 %, significantly outperforming baseline and deep learning alternatives.</div><div>• Integrates deep learning with fuzzy logic for both accurate forecasting and interpretable response planning.</div><div>• Enables uncertainty-aware reasoning by translating predictions into actionable fire management rules.</div><div>• Demonstrates superior performance across diverse environmental datasets using multi-source satellite and climate inputs.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103498"},"PeriodicalIF":1.6,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680336","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":"Investigation into the determinants of occupational safety for achieving zero accidents in multi-storey building construction projects: A Bayesian belief network approach","authors":"Ryan Pramanda ,&nbsp;Sri Gunani Partiwi ,&nbsp;Adithya Sudiarno ,&nbsp;Hamdani","doi":"10.1016/j.mex.2025.103508","DOIUrl":"10.1016/j.mex.2025.103508","url":null,"abstract":"<div><div>The Indonesian construction sector is vital for economic development but faces high accident rates because of inadequate occupational health and safety (OHS) measures. This study examines causal factors in high-rise construction accidents, focusing on human factors, organization, work environment, and payment systems. A Bayesian Belief Network (BBN) was used to evaluate OHS implementation and identify key elements for achieving zero accidents. Data were collected through a structured questionnaire completed by 28 experts assessing 277 workers across 33 critical safety variable relationships. Analysis using RStudio’s bnlearn revealed that regulatory requirements strongly influence safety design, corporate culture, and OHS management, while overly complex safety designs hinder supervision and training. Findings show that workers’ physical and mental conditions are vital for zero accidents, whereas safety awareness alone is insufficient. Improved training enhances payment systems, boosting motivation and productivity. The study recommends simplifying safety design and refining OHS procedures to reduce accident risks. The main advantages of the proposed method are:<ul><li><span>•</span><span><div>Using Bayesian Belief Network to analyse work accidents at high-rise projects.</div></span></li><li><span>•</span><span><div>Examined the human, organisational, work environment, and payment system aspects.</div></span></li><li><span>•</span><span><div>Found that simplifying safety design and improving the training are important for achieving zero accidents.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103508"},"PeriodicalIF":1.6,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663174","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}