The Asian Arowana, Scleropages formosus (Müller and Schlegel, 1844) is a large majestic freshwater teleost, crowned as the king of aquariums with its bright charismatic appearance and magnificent swimming performance. The most expensive Asian arowana is the Golden Blue-based Malayan Arowana which is endemic to the Bukit Merah Lake and Kerian River Basin, Perak, Malaysia. S. formosus has been listed as endangered by the IUCN (International Union for Conservation of Nature), regulated under Appendix 1 of the Convention of International Trade on Endangered Species (CITES) for commercial trade. Environmental DNA (eDNA) analysis has become widely accepted in biodiversity monitoring for the detection of rare and endangered species without harming any ecosystem or threatened species. Hence, the application of eDNA as wild population monitoring of S. formosus is possible for conservation and CITES enforcement program.
•
The species-specific primer of S. formosus was designed based on selected sequences obtained from GenBank
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This report presents the potential application of eDNA in the management of the Malaysian 686 CITES Act for conservation monitoring of the Asian Arowana
•
The detection and wild population monitoring is possible through the eDNA method as complementary tools
{"title":"Development and validation of a species-specific environmental DNA (eDNA) primer for endangered Asian arowana, Scleropages formosus (Teleostei: Osteoglossidae) for law enforcement and wild population monitoring","authors":"Nurul Affiqah Edora Mohd-Azhar , Haslawati Baharuddin , Mohamad-Sufiyan Salmi , Noor Faizah Ismail , Amatul-Samahah Md. Ali , Syazwan Saidin , Adibah Abu-Bakar","doi":"10.1016/j.mex.2024.103133","DOIUrl":"10.1016/j.mex.2024.103133","url":null,"abstract":"<div><div>The Asian Arowana, <em>Scleropages formosus</em> (Müller and Schlegel, 1844) is a large majestic freshwater teleost, crowned as the king of aquariums with its bright charismatic appearance and magnificent swimming performance. The most expensive Asian arowana is the Golden Blue-based Malayan Arowana which is endemic to the Bukit Merah Lake and Kerian River Basin, Perak, Malaysia. <em>S. formosus</em> has been listed as endangered by the IUCN (International Union for Conservation of Nature), regulated under Appendix 1 of the Convention of International Trade on Endangered Species (CITES) for commercial trade. Environmental DNA (eDNA) analysis has become widely accepted in biodiversity monitoring for the detection of rare and endangered species without harming any ecosystem or threatened species. Hence, the application of eDNA as wild population monitoring of <em>S. formosus</em> is possible for conservation and CITES enforcement program.<ul><li><span>•</span><span><div>The species-specific primer of <em>S. formosus</em> was designed based on selected sequences obtained from GenBank</div></span></li><li><span>•</span><span><div>This report presents the potential application of eDNA in the management of the Malaysian 686 CITES Act for conservation monitoring of the Asian Arowana</div></span></li><li><span>•</span><span><div>The detection and wild population monitoring is possible through the eDNA method as complementary tools</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103133"},"PeriodicalIF":1.6,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143047239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1016/j.mex.2024.103149
Chiharu Ichikawa , Koji Kadota
RNA-seq is a commonly employed methodology for the measurement of transcriptomes, particularly for the identification of differentially expressed genes (DEGs) between different conditions or groups. In a previous report, we described a clustering-based method for identifying DEGs, designated MBCdeg1 and MBCdeg2. and a modified version, MBCdeg3. This study presents a further improved version, designated MBCdeg4. The four versions of MBCdeg employ an R package, designated MBCluster.Seq, internally. The sole distinction between them is the manner of data normalization. MBCdeg4 employs normalization factors derived from a robust normalization algorithm, designated as DEGES. Seven competing methods were compared: the four versions of MBCdeg and three conventional R packages (edgeR, DESeq2, and TCC). MBCdeg4 demonstrated superior performance in a multitude of simulation scenarios involving RNA-seq count data. Therefore, MBCdeg4 is recommended for use in preference to the earlier versions, MBCdeg1–3.
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MBCdeg4 is a method for both identification and classification of DEGs from RNA-seq count data.
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MBCdeg4 is available as an R function and performs well in a wide variety of simulation scenarios.
{"title":"MBCdeg4: A modified clustering-based method for identifying differentially expressed genes from RNA-seq data","authors":"Chiharu Ichikawa , Koji Kadota","doi":"10.1016/j.mex.2024.103149","DOIUrl":"10.1016/j.mex.2024.103149","url":null,"abstract":"<div><div>RNA-seq is a commonly employed methodology for the measurement of transcriptomes, particularly for the identification of differentially expressed genes (DEGs) between different conditions or groups. In a previous report, we described a clustering-based method for identifying DEGs, designated MBCdeg1 and MBCdeg2. and a modified version, MBCdeg3. This study presents a further improved version, designated MBCdeg4. The four versions of MBCdeg employ an R package, designated MBCluster.Seq, internally. The sole distinction between them is the manner of data normalization. MBCdeg4 employs normalization factors derived from a robust normalization algorithm, designated as DEGES. Seven competing methods were compared: the four versions of MBCdeg and three conventional R packages (edgeR, DESeq2, and TCC). MBCdeg4 demonstrated superior performance in a multitude of simulation scenarios involving RNA-seq count data. Therefore, MBCdeg4 is recommended for use in preference to the earlier versions, MBCdeg1–3.<ul><li><span>•</span><span><div>MBCdeg4 is a method for both identification and classification of DEGs from RNA-seq count data.</div></span></li><li><span>•</span><span><div>MBCdeg4 is available as an R function and performs well in a wide variety of simulation scenarios.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103149"},"PeriodicalIF":1.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11762142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1016/j.mex.2024.103143
Sri Takshara K , Bhuvaneswari G , T.S. Pradeep Kumar
This study introduces a framework that integrates AI-driven Game-Based Language Teaching (GBLT) with advanced neuroscience to transform language education for visually impaired learners. Built on the principles of neuroplasticity and epigenetics, the approach leverages educational psychology with the help of adaptive AI to deliver personalized, gamified learning experiences that reshape neural pathways, improve memory retention, and strengthen emotional resilience. By fostering low-stress, immersive environments, it triggers positive epigenetic changes, enhancing long-term cognitive flexibility. This method article presents an interdisciplinary inclusive scientific framework that unites gamification, technology, epigenetics, and neuroscience, empowering learners and promoting sustainable well-being, while laying the groundwork for intergenerational academic and personal growth.
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To propose a teaching methodology that aims to bridge gaps in traditional and game based language teaching by integrating neuroscience and epigenetics, creating an inclusive framework tailored to meet the unique needs of visually impaired learners.
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The method emphasizes creating stress-free, rewarding educational environments that induce neural and epigenetic changes. These changes optimize gene expression to improve retention, emotional resilience, and cognitive flexibility, ensuring both immediate academic success and lifelong developmental benefits for diverse learners.
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This framework promotes cognitive, emotional, social and biological development alongside language acquisition giving a holistic mode of growth.
{"title":"The game of language learning and rewiring biocognitive receptors","authors":"Sri Takshara K , Bhuvaneswari G , T.S. Pradeep Kumar","doi":"10.1016/j.mex.2024.103143","DOIUrl":"10.1016/j.mex.2024.103143","url":null,"abstract":"<div><div>This study introduces a framework that integrates AI-driven Game-Based Language Teaching (GBLT) with advanced neuroscience to transform language education for visually impaired learners. Built on the principles of neuroplasticity and epigenetics, the approach leverages educational psychology with the help of adaptive AI to deliver personalized, gamified learning experiences that reshape neural pathways, improve memory retention, and strengthen emotional resilience. By fostering low-stress, immersive environments, it triggers positive epigenetic changes, enhancing long-term cognitive flexibility. This method article presents an interdisciplinary inclusive scientific framework that unites gamification, technology, epigenetics, and neuroscience, empowering learners and promoting sustainable well-being, while laying the groundwork for intergenerational academic and personal growth.<ul><li><span>•</span><span><div>To propose a teaching methodology that aims to bridge gaps in traditional and game based language teaching by integrating neuroscience and epigenetics, creating an inclusive framework tailored to meet the unique needs of visually impaired learners.</div></span></li><li><span>•</span><span><div>The method emphasizes creating stress-free, rewarding educational environments that induce neural and epigenetic changes. These changes optimize gene expression to improve retention, emotional resilience, and cognitive flexibility, ensuring both immediate academic success and lifelong developmental benefits for diverse learners.</div></span></li><li><span>•</span><span><div>This framework promotes cognitive, emotional, social and biological development alongside language acquisition giving a holistic mode of growth.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103143"},"PeriodicalIF":1.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1016/j.mex.2024.103148
V. Balachandar, K. Venkatesh
Forecasting student performance with precision in the educational space is paramount for creating tailor-made interventions capable to boost learning effectiveness. It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning. We developed a method that targets the common issues associated with educational datasets over imbalanced and temporal settings which is also explainable through AI features. Moreover, through adaptive hyper-parameter tuning and advanced graph neural network layers in the MSPP model allow to make output more dense representation for predictions resulting more accurate classification. The experiments results show that MSPP outperforms the other EAI&ML, MTSDA, XAI, DGNN and DLM with high accuracy 76 %, precision score of 0.79 and macro F1-score of 0.73. The model also helps to bring down the False Positive Rate (FPR) substantially at a 0.15 level, which ensures more reliable predictions for student classification.
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The model of the MSPP includes contextual information and multi-layered analysis in order to improve prediction accuracy, placing a sound basis for predicting students in different performance categories such as distinction, pass, fail or withdrawn.
•
Our approach is obviously to generalize and extract those sparse, heterogeneous academic data in the form of structured training record using domain specific preprocessing integrating with multi-class classification mechanisms that improves on precision-recall across multiple categories.
{"title":"A multi-dimensional student performance prediction model (MSPP): An advanced framework for accurate academic classification and analysis","authors":"V. Balachandar, K. Venkatesh","doi":"10.1016/j.mex.2024.103148","DOIUrl":"10.1016/j.mex.2024.103148","url":null,"abstract":"<div><div>Forecasting student performance with precision in the educational space is paramount for creating tailor-made interventions capable to boost learning effectiveness. It means most of the traditional student performance prediction models have difficulty in dealing with multi-dimensional academic data, can cause sub-optimal classification and generate a simple generalized insight. To address these challenges of the existing system, in this research we propose a new model Multi-dimensional Student Performance Prediction Model (MSPP) that is inspired by advanced data preprocessing and feature engineering techniques using deep learning. We developed a method that targets the common issues associated with educational datasets over imbalanced and temporal settings which is also explainable through AI features. Moreover, through adaptive hyper-parameter tuning and advanced graph neural network layers in the MSPP model allow to make output more dense representation for predictions resulting more accurate classification. The experiments results show that MSPP outperforms the other EAI&ML, MTSDA, XAI, DGNN and DLM with high accuracy 76 %, precision score of 0.79 and macro F1-score of 0.73. The model also helps to bring down the False Positive Rate (FPR) substantially at a 0.15 level, which ensures more reliable predictions for student classification.<ul><li><span>•</span><span><div>The model of the MSPP includes contextual information and multi-layered analysis in order to improve prediction accuracy, placing a sound basis for predicting students in different performance categories such as distinction, pass, fail or withdrawn.</div></span></li><li><span>•</span><span><div>Our approach is obviously to generalize and extract those sparse, heterogeneous academic data in the form of structured training record using domain specific preprocessing integrating with multi-class classification mechanisms that improves on precision-recall across multiple categories.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103148"},"PeriodicalIF":1.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11761817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143047236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-29DOI: 10.1016/j.mex.2024.103139
Pablo A. Nogueira-Islas , Paola V. Olguín-Rodríguez , Hugo Tlahuext-Aca , Markus F. Müller
It is assumed that social interaction like cooperation or competition takes place via synchronized interbrain activity, measurable via hyperscanning experiments. However, interbrain synchronization might also be due to common external stimuli without any genuine inter-personal interaction. In addition, a consistent experimental paradigm is required to distinguish between different modalities such as cooperation or competition. To facilitate retrospective analysis, it would also be desirable for the task that the participants have to perform not to be divided into short time intervals, but to be continuous over a longer period of time. Here we present an experimental protocol that allows
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The same paradigm for different modalities
•
Tasks demands a continuous interaction during a period of minutes
•
Contains two different control conditions to differentiate between inter-brain synchronization provoked by genuine interpersonal interactions and accidental synchronization via common external stimuli.
{"title":"Uniform hyperscanning paradigm to study cooperation and competition","authors":"Pablo A. Nogueira-Islas , Paola V. Olguín-Rodríguez , Hugo Tlahuext-Aca , Markus F. Müller","doi":"10.1016/j.mex.2024.103139","DOIUrl":"10.1016/j.mex.2024.103139","url":null,"abstract":"<div><div>It is assumed that social interaction like cooperation or competition takes place via synchronized interbrain activity, measurable via hyperscanning experiments. However, interbrain synchronization might also be due to common external stimuli without any genuine inter-personal interaction. In addition, a consistent experimental paradigm is required to distinguish between different modalities such as cooperation or competition. To facilitate retrospective analysis, it would also be desirable for the task that the participants have to perform not to be divided into short time intervals, but to be continuous over a longer period of time. Here we present an experimental protocol that allows<ul><li><span>•</span><span><div>The same paradigm for different modalities</div></span></li><li><span>•</span><span><div>Tasks demands a continuous interaction during a period of minutes</div></span></li><li><span>•</span><span><div>Contains two different control conditions to differentiate between inter-brain synchronization provoked by genuine interpersonal interactions and accidental synchronization via common external stimuli.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103139"},"PeriodicalIF":1.6,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11755012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.mex.2024.103145
Carolina Robledo-Castro , Camilo Vieira Mejía , Jennifer Chiu
The research aims to evaluate the effect of a robotics-based computational thinking program on executive functions and visuospatial skills in preschool children. Additionally, the study will explore the relationship between these three variables and early experiences with toys. The study will be a cluster-randomized controlled trial with pre- and post-intervention measures. Participants will be preschool children aged 5 to 6 years from both public and private institutions. The intervention will involve a new educational robotics device called ROVERSA and will consist of eight sessions distributed over four weeks, with the experimental group placed on a waitlist. To assess executive functions and visuospatial skills, standardized and validated neuropsychological measures will be used, including the Flanker task, the NIH Toolbox Card Sorting Test, the Corsi Block Test, the NEPSY-II Mental Rotation Test, the Pyramid of Mexico from the ENI-2 battery, and the Computational Thinking Test (CTt). Data analysis will begin with a hypothesis tested through ANOVA-MR, and linear regressions will be implemented to assess the relationship between variables. It is expected that the results will contribute to the discussion on teaching computational thinking and educational robotics as early learning experiences and their impact on the cognitive development of children.
{"title":"Exploring computational thinking, executive functions, visuospatial skills and experiences with toys in early childhood: Protocol for a controlled trial","authors":"Carolina Robledo-Castro , Camilo Vieira Mejía , Jennifer Chiu","doi":"10.1016/j.mex.2024.103145","DOIUrl":"10.1016/j.mex.2024.103145","url":null,"abstract":"<div><div>The research aims to evaluate the effect of a robotics-based computational thinking program on executive functions and visuospatial skills in preschool children. Additionally, the study will explore the relationship between these three variables and early experiences with toys. The study will be a cluster-randomized controlled trial with pre- and post-intervention measures. Participants will be preschool children aged 5 to 6 years from both public and private institutions. The intervention will involve a new educational robotics device called ROVERSA and will consist of eight sessions distributed over four weeks, with the experimental group placed on a waitlist. To assess executive functions and visuospatial skills, standardized and validated neuropsychological measures will be used, including the Flanker task, the NIH Toolbox Card Sorting Test, the Corsi Block Test, the NEPSY-II Mental Rotation Test, the Pyramid of Mexico from the ENI-2 battery, and the Computational Thinking Test (CTt). Data analysis will begin with a hypothesis tested through ANOVA-MR, and linear regressions will be implemented to assess the relationship between variables. It is expected that the results will contribute to the discussion on teaching computational thinking and educational robotics as early learning experiences and their impact on the cognitive development of children.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103145"},"PeriodicalIF":1.6,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11764239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.mex.2024.103138
María Laura Gigena , Dong Xiao , Fangzhou Li , Chengyu Wu , Yuhong Zhang
A critical challenge in ecological restoration of open-pit mine dumps in cold regions with limited topsoil resources is how to rapidly mitigate the plant growth-inhibitory effects of mineral black clay, thereby converting it into arable soil. Leveraging the high degradation capacity of coal seam-associated microorganisms on fossil carbon materials, combined with soil conditioning techniques, this study developed a microbial-based approach for modifying black clay. Seed germination experiments informed both laboratory and field trial designs. This approach focused on removing germination-inhibiting compounds, establishing a plant-compatible soil ecological environment, and employing composite strategies to reduce soil viscosity. Field experiments demonstrated that in-situ microbial modification of black clay effectively supports ecological restoration, enhances plant growth. To refine and implement this microbial-based bioremediation strategy in practical ecological restoration efforts, two key technical methods were employed:
•
A comprehensive experimental protocol was established for black clay bioremediation, covering both laboratory-scale and field test procedures, ensuring the approach can be readily adapted to diverse environmental conditions.
•
By incorporating the characteristics of local species, employing representative seed germination tests to assess plant compatibility can facilitate a rapid evaluation of the bioconversion of mineral substrates into arable soils.
{"title":"Methodological study on coal-based microbial modification of mineral black clay to overcome plant growth challenges on open-pit mine dumps in cold regions","authors":"María Laura Gigena , Dong Xiao , Fangzhou Li , Chengyu Wu , Yuhong Zhang","doi":"10.1016/j.mex.2024.103138","DOIUrl":"10.1016/j.mex.2024.103138","url":null,"abstract":"<div><div>A critical challenge in ecological restoration of open-pit mine dumps in cold regions with limited topsoil resources is how to rapidly mitigate the plant growth-inhibitory effects of mineral black clay, thereby converting it into arable soil. Leveraging the high degradation capacity of coal seam-associated microorganisms on fossil carbon materials, combined with soil conditioning techniques, this study developed a microbial-based approach for modifying black clay. Seed germination experiments informed both laboratory and field trial designs. This approach focused on removing germination-inhibiting compounds, establishing a plant-compatible soil ecological environment, and employing composite strategies to reduce soil viscosity. Field experiments demonstrated that in-situ microbial modification of black clay effectively supports ecological restoration, enhances plant growth. To refine and implement this microbial-based bioremediation strategy in practical ecological restoration efforts, two key technical methods were employed:<ul><li><span>•</span><span><div>A comprehensive experimental protocol was established for black clay bioremediation, covering both laboratory-scale and field test procedures, ensuring the approach can be readily adapted to diverse environmental conditions.</div></span></li><li><span>•</span><span><div>By incorporating the characteristics of local species, employing representative seed germination tests to assess plant compatibility can facilitate a rapid evaluation of the bioconversion of mineral substrates into arable soils.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103138"},"PeriodicalIF":1.6,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11758207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.mex.2024.103146
Walter Gomes da Silva Filho , Layza Julhia do Nascimento Moura , Arthur Barcelos Massariol Nascimento , Gabrielle Cristina Tessmann , Fabricia Silva Miranda , Vitória Caroline Reinoso de Almeida , Bárbara Vargens Broedel , Miller Lucas de Faria , Fernando Zanela da Silva Arêas
Traumatic brain injury (TBI) is a global public health condition that causes cognitive and behavioral deficits. This protocol assesses the potential of quantitative electroencephalogram (EEG) biomarkers, associated with inflammatory indicators, to predict mortality and functional recovery in patients with severe TBI. Through continuous monitoring and analysis of abnormal brain activity patterns, the protocol aims to personalize therapeutic interventions and improve patient quality of life. This randomized clinical trial includes 84 adult participants with severe TBI, followed at different stages of recovery, using validated scales for functional and predictive analysis. Traumatic brain injury (TBI) is a globally impactful public health condition characterized by initial brain injuries caused by traumatic forces, leading to cognitive and behavioral deficits. The trauma triggers inflammatory and neurochemical changes that exacerbate neuronal damage, resulting in neuropsychiatric complications. The use of electroencephalogram (EEG), particularly in its quantitative form (QEEG), is crucial for patients with severe TBI, as it allows early detection of abnormal brain activity patterns, such as slow waves, which indicate a worse prognosis. This continuous monitoring, combined with inflammatory biomarkers, guides personalized therapeutic interventions and improves the prediction of clinical outcomes, contributing to patient quality of life.
{"title":"Electroencephalogram biomarkers as predictors of mortality and functional recovery in patients with severe traumatic brain injury: Protocol study","authors":"Walter Gomes da Silva Filho , Layza Julhia do Nascimento Moura , Arthur Barcelos Massariol Nascimento , Gabrielle Cristina Tessmann , Fabricia Silva Miranda , Vitória Caroline Reinoso de Almeida , Bárbara Vargens Broedel , Miller Lucas de Faria , Fernando Zanela da Silva Arêas","doi":"10.1016/j.mex.2024.103146","DOIUrl":"10.1016/j.mex.2024.103146","url":null,"abstract":"<div><div>Traumatic brain injury (TBI) is a global public health condition that causes cognitive and behavioral deficits. This protocol assesses the potential of quantitative electroencephalogram (EEG) biomarkers, associated with inflammatory indicators, to predict mortality and functional recovery in patients with severe TBI. Through continuous monitoring and analysis of abnormal brain activity patterns, the protocol aims to personalize therapeutic interventions and improve patient quality of life. This randomized clinical trial includes 84 adult participants with severe TBI, followed at different stages of recovery, using validated scales for functional and predictive analysis. Traumatic brain injury (TBI) is a globally impactful public health condition characterized by initial brain injuries caused by traumatic forces, leading to cognitive and behavioral deficits. The trauma triggers inflammatory and neurochemical changes that exacerbate neuronal damage, resulting in neuropsychiatric complications. The use of electroencephalogram (EEG), particularly in its quantitative form (QEEG), is crucial for patients with severe TBI, as it allows early detection of abnormal brain activity patterns, such as slow waves, which indicate a worse prognosis. This continuous monitoring, combined with inflammatory biomarkers, guides personalized therapeutic interventions and improves the prediction of clinical outcomes, contributing to patient quality of life.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103146"},"PeriodicalIF":1.6,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11773473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-28DOI: 10.1016/j.mex.2024.103134
Arthur Cousson , Anne-Laure Pablo , Laurent Cournac , Gabin Piton , Damien Dezette , Agnès Robin , Elisa Taschen , Laetitia Bernard
Soil microbes are among the most abundant and diverse organisms on Earth but remain poorly characterized. New technologies have made possible to sequence the DNA of uncultivated microorganisms in soil and other complex ecosystems. Genome assembly is crucial for understanding their functional potential. Nanopore sequencing technologies allow to sequence long DNA fragments, optimizing production of metagenome-assembled genomes compared to short-read technology. Extracting DNA with a very high purity and high molecular weight is key to get the most out of this long read technologies. Here we present two extraction protocols to get DNA with high purity. First protocol is optimized to reach DNA quality suiting Nanopore shotgun metagenomics. It uses a non-toxic centrifugation gradient to separate bacterial cells from soil to extract DNA directly on cells. The median length of the acquired DNA sequences (N50) was 3 to 7 times greater than previously published in the literature, achieving an N50 of ∼14 kb. The other, a modification of a commercially available MP Biomedical DNA extraction kit, yielded high-purity DNA for full-length 16S Oxford Nanopore metabarcoding, with an N50 of ∼8 kb. The MP-based protocol achieves higher yields of ultra-pure DNA compared to the Nycodenz protocol, at the expense of shorter fragment lengths.
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Pub Date : 2024-12-27DOI: 10.1016/j.mex.2024.103137
K. Schoch , M. Bickel , C. Liedtke , F. Hemmert
Project-based learning, with its emphasis on ‘learning by doing’, is the dominant teaching method in industrial design. Learners are supposed to be motivated to tackle complex problems such as those in the dynamic field of sustainability. However, it is still unclear how the process of increasing motivation within projects can be systematically targeted for specific sustainability challenges and directed towards potential later pro-environmental behavior.
The project-based learning method presented in this paper, framed by a normative decision-making model, aims to intrinsically motivate industrial design students to engage in the exemplary circular economy field of metal recycling and at the same time promote necessary professional competencies on the metal, alloy, product and system level. It is demonstrated which specific intervention measures can be suitable to achieve this goal and how they can be methodically employed. Preliminary quantitative evaluation results indicate that the project-based learning method can indeed strongly motivate the target group.
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Connection to a normative decision-making model from social psychology for the domain of environmental protection as a conceptual framework
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Consideration of intervention measures at various levels (metal, alloy, product, system)
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Use of generative toolkits to translate the acquired knowledge into practical application during design tasks
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