Pub Date : 2026-06-01Epub Date: 2026-01-06DOI: 10.1016/j.mex.2026.103787
Alexandra N. Buntin-Nakamura, Daniel Quintana
Head fixation combined with cranial window, GRIN lens, or prism implantation are common techniques in systems neuroscience for real-time imaging of neural activity. A critical step in these experiments is the removal of these implants and subsequent histological processing to assess tissue integrity and verify opsin expression. However, removing implants from the dorsal surface of the skull often causes traumatic damage to the underlying cortex. This study introduces a novel technique for intact brain excision that removes dorsal implants while preserving cortical integrity. Instead of the conventional dorsal approach, which often risks cortical damage, the skull is resected from the ventral side, allowing the implant to remain in place for post-mortem analysis.
This approach:
•
Enables dorsal implants to remain embedded in the brain during fixation.
•
Improves reproducibility by standardizing the extraction method, reducing variability introduced by traditional dorsal implant removal.
•
Preserves tissue integrity that would otherwise be compromised by removing the implant from fresh brain tissue.
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Pub Date : 2026-06-01Epub Date: 2026-01-07DOI: 10.1016/j.mex.2026.103792
Ambreen Gul , Abdul Qayyum Rao , Allah Bakhsh
RNA extraction from polyphenolic-rich plants poses significant challenges, demanding precise sample handling and rigorous experimental conditions to ensure high yield and quality of the RNA. Numerous kits are available for RNA extraction from various sources, however extracting RNA from phenolic-rich plants requires special attention. On the other hand, manual extraction methods are time-consuming and involve harsh chemicals (LiCl, phenol, guanidine thiocyanate). To address this issue, we have improvised and compared three strategies for RNA extraction. We found that CTAB-based extraction buffer produces high-quality RNA from cotton, followed by ammonium acetate purification, within two hours. Column purification (DirectZol RNA purification kit) followed by CTAB extraction step not only accelerates the RNA purification process (∼30 min) but produces high-quality RNA suitable for various downstream applications such as real-time quantitative PCR, sequencing, and RNA library preparation. The RNA yield ranged between 80-100µg/100mg for CTAB-ammonium acetate as compared to up to 12µg/100mg for guanidium thiocyanate-ammonium acetate-based extraction from cotyledonary leaves of cotton. RNA purification from mature cotton leaves was unsuccessful with guanidinium thiocyanate. The downstream applications like qPCR and the sequencing results depicted the contaminant-free RNA from phenolic-rich mature cotton leaves.
The modified method is rapid and could be completed within 2 hours for completely manual procedure
For extra fast purification CTAB-buffer extraction can be combined with RNA purification kit (∼30-40 min)
The modified method yields high quality and phenol-free RNA for downstream applications like real time quantitative PCR, sequencing, and RNA library preparation.
{"title":"Development of a rapid and modified total RNA extraction method from polyphenolic-rich Gossypium hirsutum","authors":"Ambreen Gul , Abdul Qayyum Rao , Allah Bakhsh","doi":"10.1016/j.mex.2026.103792","DOIUrl":"10.1016/j.mex.2026.103792","url":null,"abstract":"<div><div>RNA extraction from polyphenolic-rich plants poses significant challenges, demanding precise sample handling and rigorous experimental conditions to ensure high yield and quality of the RNA. Numerous kits are available for RNA extraction from various sources, however extracting RNA from phenolic-rich plants requires special attention. On the other hand, manual extraction methods are time-consuming and involve harsh chemicals (LiCl, phenol, guanidine thiocyanate). To address this issue, we have improvised and compared three strategies for RNA extraction. We found that CTAB-based extraction buffer produces high-quality RNA from cotton, followed by ammonium acetate purification, within two hours. Column purification (DirectZol RNA purification kit) followed by CTAB extraction step not only accelerates the RNA purification process (∼30 min) but produces high-quality RNA suitable for various downstream applications such as real-time quantitative PCR, sequencing, and RNA library preparation. The RNA yield ranged between 80-100µg/100mg for CTAB-ammonium acetate as compared to up to 12µg/100mg for guanidium thiocyanate-ammonium acetate-based extraction from cotyledonary leaves of cotton. RNA purification from mature cotton leaves was unsuccessful with guanidinium thiocyanate. The downstream applications like qPCR and the sequencing results depicted the contaminant-free RNA from phenolic-rich mature cotton leaves.</div><div>The modified method is rapid and could be completed within 2 hours for completely manual procedure</div><div>For extra fast purification CTAB-buffer extraction can be combined with RNA purification kit (∼30-40 min)</div><div>The modified method yields high quality and phenol-free RNA for downstream applications like real time quantitative PCR, sequencing, and RNA library preparation.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103792"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977505","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}
Pub Date : 2026-06-01Epub Date: 2025-12-12DOI: 10.1016/j.mex.2025.103757
Sayed Saber , Abdullah A. Alahmari
<div><div>This study presents a novel numerical framework for simulating glucose-insulin regulatory dynamics using the Caputo-Fabrizio (CF) fractal-fractional operator with both constant and variable fractional orders. The model incorporates an exponential decay kernel to capture memory and hereditary effects in metabolic regulation. A Newton interpolation-based numerical scheme is developed to approximate the CF-FF derivatives, ensuring computational stability and accuracy. For the variable-order formulation, the fractional order <span><math><mrow><mi>β</mi><mo>(</mo><mi>t</mi><mo>)</mo></mrow></math></span> dynamically evolves with time, reflecting physiological variability typically observed during intravenous glucose tolerance tests (IVGTT). Numerical experiments reproduce physiologically realistic glucose-insulin oscillations and demonstrate how feedback control stabilizes chaotic metabolic behavior. The results are based entirely on simulation evidence calibrated within clinically reported parameter ranges, providing conceptual validation rather than direct patient-data comparison. The proposed approach bridges mathematical fractional calculus with biomedical applications, offering new insights for personalized diabetes management and adaptive glucose control strategies.<ul><li><span>•</span><span><div>Fractal-fractional model formulation capturing glucose-insulin memory and adaptation</div></span></li><li><span>•</span><span><div>Stable numerical scheme using Newton interpolation for accurate fractional integration</div></span></li><li><span>•</span><span><div>Linear feedback control applied to regulate chaotic glucose-insulin dynamics</div></span></li><li><span>•</span><span><div>Numerical Methodology for glucose-insulin dynamics. Our investigation of the fractal-fractional glucose-insulin system employs the following analytical framework:</div></span></li><li><span>•</span><span><div>Model Development: We formulate a fractal-fractional-order extension of the minimal glucose insulin model, incorporating an exponential decay type kernel to capture the system's memory effects and anomalous diffusion characteristics inherent in metabolic processes. The model accounts for both insulin-dependent and independent glucose utilization dynamics.</div></span></li><li><span>•</span><span><div>Computational Implementation: We develop a novel numerical solver based on Newton's interpolation polynomials, implementing the Atangana-Seda fractal-fractional derivative formulation. This method provides an efficient computational framework for solving the coupled nonlinear fractional differential equations while maintaining numerical stability across different fractional orders.</div></span></li><li><span>•</span><span><div>The purpose of this section is to define a mathematical model to study the dynamic behavior of glucose-insulin physiology.</div></span></li><li><span>•</span><span><div>With the Adams-Bashforth-Moulton numerical scheme, we compute the Lyap
{"title":"Existence, Stability, and Control of Glucose-Insulin Dynamics via Caputo-Fabrizio Fractal-Fractional Operators","authors":"Sayed Saber , Abdullah A. Alahmari","doi":"10.1016/j.mex.2025.103757","DOIUrl":"10.1016/j.mex.2025.103757","url":null,"abstract":"<div><div>This study presents a novel numerical framework for simulating glucose-insulin regulatory dynamics using the Caputo-Fabrizio (CF) fractal-fractional operator with both constant and variable fractional orders. The model incorporates an exponential decay kernel to capture memory and hereditary effects in metabolic regulation. A Newton interpolation-based numerical scheme is developed to approximate the CF-FF derivatives, ensuring computational stability and accuracy. For the variable-order formulation, the fractional order <span><math><mrow><mi>β</mi><mo>(</mo><mi>t</mi><mo>)</mo></mrow></math></span> dynamically evolves with time, reflecting physiological variability typically observed during intravenous glucose tolerance tests (IVGTT). Numerical experiments reproduce physiologically realistic glucose-insulin oscillations and demonstrate how feedback control stabilizes chaotic metabolic behavior. The results are based entirely on simulation evidence calibrated within clinically reported parameter ranges, providing conceptual validation rather than direct patient-data comparison. The proposed approach bridges mathematical fractional calculus with biomedical applications, offering new insights for personalized diabetes management and adaptive glucose control strategies.<ul><li><span>•</span><span><div>Fractal-fractional model formulation capturing glucose-insulin memory and adaptation</div></span></li><li><span>•</span><span><div>Stable numerical scheme using Newton interpolation for accurate fractional integration</div></span></li><li><span>•</span><span><div>Linear feedback control applied to regulate chaotic glucose-insulin dynamics</div></span></li><li><span>•</span><span><div>Numerical Methodology for glucose-insulin dynamics. Our investigation of the fractal-fractional glucose-insulin system employs the following analytical framework:</div></span></li><li><span>•</span><span><div>Model Development: We formulate a fractal-fractional-order extension of the minimal glucose insulin model, incorporating an exponential decay type kernel to capture the system's memory effects and anomalous diffusion characteristics inherent in metabolic processes. The model accounts for both insulin-dependent and independent glucose utilization dynamics.</div></span></li><li><span>•</span><span><div>Computational Implementation: We develop a novel numerical solver based on Newton's interpolation polynomials, implementing the Atangana-Seda fractal-fractional derivative formulation. This method provides an efficient computational framework for solving the coupled nonlinear fractional differential equations while maintaining numerical stability across different fractional orders.</div></span></li><li><span>•</span><span><div>The purpose of this section is to define a mathematical model to study the dynamic behavior of glucose-insulin physiology.</div></span></li><li><span>•</span><span><div>With the Adams-Bashforth-Moulton numerical scheme, we compute the Lyap","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103757"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798161","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}
Tamil Nadu conducted the first round of WHO STEPS survey in 2020 to assess non-communicable disease (NCD) risk factors. In 2021, subsequently the state launched the Makkalai Thedi Maruthuvam (MTM) initiative to deliver home-based care for individuals with NCDs. To evaluate the effectiveness of the program, we designed a population-based survey in 2023–2024 using electoral booth sampling as a primary sampling unit to assess control rates of hypertension and diabetes among individuals aged 18–69 years using the same WHO STEPS approach. Secondary objective was to estimate the prevalence of behavioural and biological NCD risk factors. A multistage cluster sampling method was used across the Tamil Nadu. A total of 8880 participants were selected from 148 clusters. One eligible adult per household was selected using the KISH method. Data were collected through interviews such as sociodemographic, anthropometric and biological parameters. This survey is the first in Tamil Nadu to use updated electoral data as a sampling frame to estimate the care cascade of the hypertension and diabetes. Although limited by its cross-sectional design and lack of biochemical markers test like HbA1c and cholesterol, the survey offers a practical and scalable model for NCD surveillance in India.
•
This study used the updated electoral booth data as primary sampling units in the absence of recent census data (India) to ensure the population representativeness.
•
Comprehensive assessment of hypertension and diabetes care cascade including prevalence, awareness, treatment and control rates among the individual aged 18–69 years.
•
Designed to evaluate the impact of Makkalai Thedi Maruthuvam (MTM) scheme and support to evidence based NCD intervention in Tamil Nadu.
{"title":"A cross-sectional study on hypertension and diabetes care cascade and prevalence of noncommunicable diseases risk factors in Tamil Nadu: rationale and methods","authors":"Archana Ramalingam , Joshua Chadwick , Mosoniro Kriina , Manjula Devi Neelavanan , Anusuya Govindan , Devendhiran R , Vettrichelvan Venkatasamy , Emily Devasagayam , Surya Joseph , Swathy Madhusoodanan , Bhoopathy Kangusamy , Sabarinathan Ramaswamy , Elavarasu Govindasamy , Kalaimani I , Ashok Kumar Paparaju , Chokkalingam D , Dinesh Kumar , Daniel Rajasekar , Augustine Duraisamy , Sudharshini Subramaniam , Manoj Murekhar","doi":"10.1016/j.mex.2025.103753","DOIUrl":"10.1016/j.mex.2025.103753","url":null,"abstract":"<div><div>Tamil Nadu conducted the first round of WHO STEPS survey in 2020 to assess non-communicable disease (NCD) risk factors. In 2021, subsequently the state launched the Makkalai Thedi Maruthuvam (MTM) initiative to deliver home-based care for individuals with NCDs. To evaluate the effectiveness of the program, we designed a population-based survey in 2023–2024 using electoral booth sampling as a primary sampling unit to assess control rates of hypertension and diabetes among individuals aged 18–69 years using the same WHO STEPS approach. Secondary objective was to estimate the prevalence of behavioural and biological NCD risk factors. A multistage cluster sampling method was used across the Tamil Nadu. A total of 8880 participants were selected from 148 clusters. One eligible adult per household was selected using the KISH method. Data were collected through interviews such as sociodemographic, anthropometric and biological parameters. This survey is the first in Tamil Nadu to use updated electoral data as a sampling frame to estimate the care cascade of the hypertension and diabetes. Although limited by its cross-sectional design and lack of biochemical markers test like HbA1c and cholesterol, the survey offers a practical and scalable model for NCD surveillance in India.<ul><li><span>•</span><span><div>This study used the updated electoral booth data as primary sampling units in the absence of recent census data (India) to ensure the population representativeness.</div></span></li><li><span>•</span><span><div>Comprehensive assessment of hypertension and diabetes care cascade including prevalence, awareness, treatment and control rates among the individual aged 18–69 years.</div></span></li><li><span>•</span><span><div>Designed to evaluate the impact of Makkalai Thedi Maruthuvam (MTM) scheme and support to evidence based NCD intervention in Tamil Nadu.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103753"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798168","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}
Pub Date : 2026-06-01Epub Date: 2025-12-05DOI: 10.1016/j.mex.2025.103750
Ricvan Dana Nindrea , Linda Rosalina , Milya Novera , Long Chiau Ming , Nissa Prima Sari , Nabil Aresto Avilla , Fanisha Anugrah Rahmadhani Putri , Nailah Putri Rivani
Preterm and low birth weight (LBW) infants face elevated health risks and require specialized care. Maternal postnatal depression (PND) and the quality of mother–infant bonding are critical determinants of caregiving practices and neonatal outcomes. However, practical, validated methods for assessing these constructs remain limited within the Indonesian clinical and research context. This study presents a protocol for assessing PND and bonding among mothers of preterm and LBW infants in Indonesia. A community-based cross-sectional design was implemented across three districts in West Sumatra. A total of 255 mothers of preterm or LBW infants were selected using multistage random sampling. PND was measured using the validated 10-item Edinburgh Postnatal Depression Scale (EPDS), with a cut-off score of 12/13 indicating significant depression. Mother–infant bonding was assessed with a culturally adapted 10-item bonding questionnaire. Maternal practices were evaluated using an 8-item checklist covering breastfeeding, Kangaroo Mother Care, immunization, and use of maternal–child health records. Instruments underwent expert review, translation and back-translation, and pilot testing to ensure validity and reliability (Cronbach’s α: 0.75–0.90). The primary endpoints included the identification of maternal PND, the quality of bonding, and maternal adherence to essential infant care practices. Data collection followed a standardized interviewer protocol, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The protocol proved feasible in community settings and provides a replicable method to evaluate maternal PND and bonding, with potential to inform interventions that enhance neonatal care outcomes.
{"title":"Assessing maternal postnatal depression, bonding and practices in mothers of preterm and low birth weight infants in Indonesia","authors":"Ricvan Dana Nindrea , Linda Rosalina , Milya Novera , Long Chiau Ming , Nissa Prima Sari , Nabil Aresto Avilla , Fanisha Anugrah Rahmadhani Putri , Nailah Putri Rivani","doi":"10.1016/j.mex.2025.103750","DOIUrl":"10.1016/j.mex.2025.103750","url":null,"abstract":"<div><div>Preterm and low birth weight (LBW) infants face elevated health risks and require specialized care. Maternal postnatal depression (PND) and the quality of mother–infant bonding are critical determinants of caregiving practices and neonatal outcomes. However, practical, validated methods for assessing these constructs remain limited within the Indonesian clinical and research context. This study presents a protocol for assessing PND and bonding among mothers of preterm and LBW infants in Indonesia. A community-based cross-sectional design was implemented across three districts in West Sumatra. A total of 255 mothers of preterm or LBW infants were selected using multistage random sampling. PND was measured using the validated 10-item Edinburgh Postnatal Depression Scale (EPDS), with a cut-off score of 12/13 indicating significant depression. Mother–infant bonding was assessed with a culturally adapted 10-item bonding questionnaire. Maternal practices were evaluated using an 8-item checklist covering breastfeeding, Kangaroo Mother Care, immunization, and use of maternal–child health records. Instruments underwent expert review, translation and back-translation, and pilot testing to ensure validity and reliability (Cronbach’s α: 0.75–0.90). The primary endpoints included the identification of maternal PND, the quality of bonding, and maternal adherence to essential infant care practices. Data collection followed a standardized interviewer protocol, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The protocol proved feasible in community settings and provides a replicable method to evaluate maternal PND and bonding, with potential to inform interventions that enhance neonatal care outcomes.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103750"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145749539","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}
Pub Date : 2026-06-01Epub Date: 2025-12-11DOI: 10.1016/j.mex.2025.103762
Prabhu Shankar B , RajKumar N , Jayavadivel Ravi , Viji C , Gobinath J , Govindharaj I , Dinesh Kumar K , Elango Muthusamy
In cloud computing, it remains difficult to make data available in a cloud service such that the data is replicated and maintained consistently across various data centers. Traditional replication systems are sufficient, even though they take too long to process, cause significant data transfers, and face problems with final data consistency. This work presents a new method named Quantum Entanglement-Based Replication Algorithm (QERA), which makes use of quantum entanglement to ensure quick and high-performance synchronization of cloud data across all nodes. In this proposed work, the QERA approach encodes data changes in the primary cloud node onto quantum states and entangled qubit pairs to the related replica nodes. As a result, any change is quickly shown on all replicas without the usual overhead and delay of message broadcasts. It simulates how QERA is designed to decrease latency, promote consistency, and make better use of resources in cloud environments. This paper creates a theoretical framework using IBM Qiskit and Microsoft Quantum Development Kit simulators to compare classical and quantum baseline algorithms. The results show that QERA may greatly enhance the way updates and replications are managed across many cloud systems.
It demonstrates how QERA can ensure a very synchronized replication among the remote cloud nodes.
Employs a qubit pair entangled to minimize latency and decrease bandwidth expenses as it goes through updates.
Combines the idea of quantum teleportation with methods of non-invasive verification made to maintain the integrity of the state without altering the quantum system.
{"title":"Efficient data replication in distributed clouds via quantum entanglement algorithms","authors":"Prabhu Shankar B , RajKumar N , Jayavadivel Ravi , Viji C , Gobinath J , Govindharaj I , Dinesh Kumar K , Elango Muthusamy","doi":"10.1016/j.mex.2025.103762","DOIUrl":"10.1016/j.mex.2025.103762","url":null,"abstract":"<div><div>In cloud computing, it remains difficult to make data available in a cloud service such that the data is replicated and maintained consistently across various data centers. Traditional replication systems are sufficient, even though they take too long to process, cause significant data transfers, and face problems with final data consistency. This work presents a new method named Quantum Entanglement-Based Replication Algorithm (QERA), which makes use of quantum entanglement to ensure quick and high-performance synchronization of cloud data across all nodes. In this proposed work, the QERA approach encodes data changes in the primary cloud node onto quantum states and entangled qubit pairs to the related replica nodes. As a result, any change is quickly shown on all replicas without the usual overhead and delay of message broadcasts. It simulates how QERA is designed to decrease latency, promote consistency, and make better use of resources in cloud environments. This paper creates a theoretical framework using IBM Qiskit and Microsoft Quantum Development Kit simulators to compare classical and quantum baseline algorithms. The results show that QERA may greatly enhance the way updates and replications are managed across many cloud systems.</div><div>It demonstrates how QERA can ensure a very synchronized replication among the remote cloud nodes.</div><div>Employs a qubit pair entangled to minimize latency and decrease bandwidth expenses as it goes through updates.</div><div>Combines the idea of quantum teleportation with methods of non-invasive verification made to maintain the integrity of the state without altering the quantum system.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103762"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145798570","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}
Pub Date : 2026-06-01Epub Date: 2026-06-06DOI: 10.1016/j.mex.2026.103992
Sghaier Guizani , Sneha Xavier , Amal Ajayan , Asem I. Alalwan , Habib Hamam
Intrusion detection pipelines often achieve strong predictive performance, yet many remain difficult to reproduce because preprocessing, model configuration, explainability integration, graph construction, and decision analysis are not documented as a unified, auditable workflow. This article presents a reproducible method for forensic-ready intrusion detection that integrates leakage-safe stratified partitioning, deterministic preprocessing, Random Forest-based multi-class prediction, explainability via SHAP and LIME, prediction-aware interaction graph construction, graph-based node and flow ranking, and counterfactual reasoning within a single executable pipeline. The method was implemented on the benchmark TON-IoT dataset, and the complete workflow was packaged with reproducible outputs and intermediate artifacts supporting verification and reuse. By combining predictive modeling, explainability, structural graph reasoning, and intervention-oriented counterfactual analysis, the proposed method provides a unified procedure for building and analyzing intrusion detection systems, with explicit control over data partitioning, feature transformation, model behavior, and forensic decision-making.
• A leakage-safe intrusion detection workflow was developed using stratified train–test partitioning with strict separation of contextual identifiers from predictive features and deterministic preprocessing for reproducible model training.
• A prediction-aware analytical framework was constructed by integrating SHAP and LIME-based explainability, hybrid suspicion scoring, and directed interaction graph modeling to enable graph-based ranking of suspicious nodes and flows.
• Counterfactual reasoning using DiCE was incorporated to analyze decision-sensitive feature perturbations, and the complete pipeline was released with artifact-backed outputs to support reproducible forensic and cyber defense studies.
{"title":"A method for forensic-ready intrusion detection using explainable learning, prediction-aware graph modeling, and counterfactual analysis","authors":"Sghaier Guizani , Sneha Xavier , Amal Ajayan , Asem I. Alalwan , Habib Hamam","doi":"10.1016/j.mex.2026.103992","DOIUrl":"10.1016/j.mex.2026.103992","url":null,"abstract":"<div><div>Intrusion detection pipelines often achieve strong predictive performance, yet many remain difficult to reproduce because preprocessing, model configuration, explainability integration, graph construction, and decision analysis are not documented as a unified, auditable workflow. This article presents a reproducible method for forensic-ready intrusion detection that integrates leakage-safe stratified partitioning, deterministic preprocessing, Random Forest-based multi-class prediction, explainability via SHAP and LIME, prediction-aware interaction graph construction, graph-based node and flow ranking, and counterfactual reasoning within a single executable pipeline. The method was implemented on the benchmark TON-IoT dataset, and the complete workflow was packaged with reproducible outputs and intermediate artifacts supporting verification and reuse. By combining predictive modeling, explainability, structural graph reasoning, and intervention-oriented counterfactual analysis, the proposed method provides a unified procedure for building and analyzing intrusion detection systems, with explicit control over data partitioning, feature transformation, model behavior, and forensic decision-making.</div><div>• A leakage-safe intrusion detection workflow was developed using stratified train–test partitioning with strict separation of contextual identifiers from predictive features and deterministic preprocessing for reproducible model training.</div><div>• A prediction-aware analytical framework was constructed by integrating SHAP and LIME-based explainability, hybrid suspicion scoring, and directed interaction graph modeling to enable graph-based ranking of suspicious nodes and flows.</div><div>• Counterfactual reasoning using DiCE was incorporated to analyze decision-sensitive feature perturbations, and the complete pipeline was released with artifact-backed outputs to support reproducible forensic and cyber defense studies.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103992"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241445","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}
Pub Date : 2026-06-01Epub Date: 2025-12-14DOI: 10.1016/j.mex.2025.103766
V. Bilro , A. Duarte Martins , T. Sobral , J.A. Parraca , M.I. Varela-Silva , N. Batalha
The present study outlines the progressive power program (PPP), a structured exercise protocol developed for adults with overweight and obesity. The PPP integrates high-intensity interval training (HIIT), high-intensity functional training (HIFT), and moderate-intensity continuous training (MICT) within a progressive 12-week framework. Participants complete three 50-minute sessions per week, delivered either face-to-face or remotely, using bodyweight exercises and self-regulated pacing guided by heart rate monitoring and perceived exertion.
The protocol is described in sufficient detail to ensure reproducibility and adaptability across diverse clinical, community, and online settings. Emphasising accessibility, safety, and participant autonomy, the PPP aims to reduce barriers related to equipment, supervision, and location. Standardised procedures for anthropometric, functional, and behavioural assessments reinforce methodological rigour.
By combining evidence-based exercise modalities with scalable delivery strategies, the PPP offers a resource-efficient and adaptable approach to promoting physical activity and supporting weight management. Its methodological design aligns with public health priorities by addressing physical inactivity, reducing sedentary behaviour, and improving health outcomes in at-risk populations.
•
Designed a progressive 12-week exercise protocol incorporating HIIT, HIFT, and MICT modalities.
•
Assessed feasibility and reach by comparing face-to-face and remote delivery formats.
•
Evaluated physical, functional, and behavioural outcomes before and after the intervention.
{"title":"The progressive power program: An exercise intervention to improve health outcomes in adults with overweight and obesity","authors":"V. Bilro , A. Duarte Martins , T. Sobral , J.A. Parraca , M.I. Varela-Silva , N. Batalha","doi":"10.1016/j.mex.2025.103766","DOIUrl":"10.1016/j.mex.2025.103766","url":null,"abstract":"<div><div>The present study outlines the progressive power program (PPP), a structured exercise protocol developed for adults with overweight and obesity. The PPP integrates high-intensity interval training (HIIT), high-intensity functional training (HIFT), and moderate-intensity continuous training (MICT) within a progressive 12-week framework. Participants complete three 50-minute sessions per week, delivered either face-to-face or remotely, using bodyweight exercises and self-regulated pacing guided by heart rate monitoring and perceived exertion.</div><div>The protocol is described in sufficient detail to ensure reproducibility and adaptability across diverse clinical, community, and online settings. Emphasising accessibility, safety, and participant autonomy, the PPP aims to reduce barriers related to equipment, supervision, and location. Standardised procedures for anthropometric, functional, and behavioural assessments reinforce methodological rigour.</div><div>By combining evidence-based exercise modalities with scalable delivery strategies, the PPP offers a resource-efficient and adaptable approach to promoting physical activity and supporting weight management. Its methodological design aligns with public health priorities by addressing physical inactivity, reducing sedentary behaviour, and improving health outcomes in at-risk populations.<ul><li><span>•</span><span><div>Designed a progressive 12-week exercise protocol incorporating HIIT, HIFT, and MICT modalities.</div></span></li><li><span>•</span><span><div>Assessed feasibility and reach by comparing face-to-face and remote delivery formats.</div></span></li><li><span>•</span><span><div>Evaluated physical, functional, and behavioural outcomes before and after the intervention.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103766"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926359","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}
Count data time series, characterized by non-negative integer values, frequently arise across diverse domains, including finance, public health, economics, epidemiology, and environmental sciences. Such series often exhibit characteristics such as equidispersion, overdispersion, underdispersion, and zero-inflation/deflation. Failure to appropriately account for these features can result in biased parameter estimates and misleading statistical inference. This review presents a comprehensive overview of recent methodological developments in integer-valued autoregressive (INAR) models, with particular emphasis on thinning operators, estimation methods, and model extensions. A systematic literature search was conducted using electronic databases, including Scopus and Google Scholar, to identify relevant studies published between 2010 and 2024. Recent research has primarily focused on the development of novel thinning operators and flexible innovation distributions aimed at constructing unified modeling frameworks capable of accommodating multiple characteristics of count data simultaneously. This review highlights prevailing research trends, identifies existing methodological gaps, and outlines promising directions for future research in count data time series modeling.
{"title":"Recent advancements in integer-valued autoregressive models for count data time series: A comprehensive review","authors":"Vinitha Serrao, Satyanarayana Poojari, Asha Kamath","doi":"10.1016/j.mex.2026.103805","DOIUrl":"10.1016/j.mex.2026.103805","url":null,"abstract":"<div><div>Count data time series, characterized by non-negative integer values, frequently arise across diverse domains, including finance, public health, economics, epidemiology, and environmental sciences. Such series often exhibit characteristics such as equidispersion, overdispersion, underdispersion, and zero-inflation/deflation. Failure to appropriately account for these features can result in biased parameter estimates and misleading statistical inference. This review presents a comprehensive overview of recent methodological developments in integer-valued autoregressive (INAR) models, with particular emphasis on thinning operators, estimation methods, and model extensions. A systematic literature search was conducted using electronic databases, including Scopus and Google Scholar, to identify relevant studies published between 2010 and 2024. Recent research has primarily focused on the development of novel thinning operators and flexible innovation distributions aimed at constructing unified modeling frameworks capable of accommodating multiple characteristics of count data simultaneously. This review highlights prevailing research trends, identifies existing methodological gaps, and outlines promising directions for future research in count data time series modeling.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103805"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077709","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}
Pub Date : 2026-06-01Epub Date: 2026-01-07DOI: 10.1016/j.mex.2026.103788
M. Alejandra Estrada-Silva , Javier Cruz-Rodríguez , Gabriela Silva-Luna , Jimena N. Torres-Mendoza , Enrique Luna-Tenorio , Nabil I. Luna-Viramontes , Iris Dalia Vázquez-Vargas , Brenda Granados-Rivas , José Luna-Muñoz , Gonzalo Flores , Linda Garcés Ramírez , Fidel De la Cruz-López
From an evolutionary perspective, all organisms have two purposes: self-preservation and reproduction, in which immobility responses (IR) are implicated, these are ancestral survival strategies used to avoid serious injury or death. Immobility responses are adaptive reactions present in many species across virtually the entire phylogenetic spectrum, characterized by behavioral arrest and reduced responsiveness, serving evolutionary roles in prey–predator, mating, and offspring-carrying contexts. Several brain structures—including the brainstem, limbic system, striatum, and cortex—modulate IR by integrating fear, motor, and sensory processes; moreover, distinct drugs such as antipsychotics and morphine can induce IR through monoaminergic and endorphinic pathways. Immobility responses have been referred to by various terms, including animal hypnosis, catalepsy, catatonia, cataplexy, death feigning, and dorsal immobility, among others. The most commonly employed laboratory methods for inducing and replicating immobility responses are described herein. Experimental animal models reported in this work (frog, iguana, duck, rabbit, and rat) reproduce IR under controlled conditions, providing valuable insights into motor inhibition, defensive behaviors, and catatonic states.
{"title":"Comprehensive methodology for evaluating the immobility responses in laboratory animals","authors":"M. Alejandra Estrada-Silva , Javier Cruz-Rodríguez , Gabriela Silva-Luna , Jimena N. Torres-Mendoza , Enrique Luna-Tenorio , Nabil I. Luna-Viramontes , Iris Dalia Vázquez-Vargas , Brenda Granados-Rivas , José Luna-Muñoz , Gonzalo Flores , Linda Garcés Ramírez , Fidel De la Cruz-López","doi":"10.1016/j.mex.2026.103788","DOIUrl":"10.1016/j.mex.2026.103788","url":null,"abstract":"<div><div>From an evolutionary perspective, all organisms have two purposes: self-preservation and reproduction, in which immobility responses (IR) are implicated, these are ancestral survival strategies used to avoid serious injury or death. Immobility responses are adaptive reactions present in many species across virtually the entire phylogenetic spectrum, characterized by behavioral arrest and reduced responsiveness, serving evolutionary roles in prey–predator, mating, and offspring-carrying contexts. Several brain structures—including the brainstem, limbic system, striatum, and cortex—modulate IR by integrating fear, motor, and sensory processes; moreover, distinct drugs such as antipsychotics and morphine can induce IR through monoaminergic and endorphinic pathways. Immobility responses have been referred to by various terms, including animal hypnosis, catalepsy, catatonia, cataplexy, death feigning, and dorsal immobility, among others. The most commonly employed laboratory methods for inducing and replicating immobility responses are described herein. Experimental animal models reported in this work (frog, iguana, duck, rabbit, and rat) reproduce IR under controlled conditions, providing valuable insights into motor inhibition, defensive behaviors, and catatonic states.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"16 ","pages":"Article 103788"},"PeriodicalIF":1.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034288","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}
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