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Neurodegenerative disorders such as Alzheimer's and Parkinson's diseases a re increasing in prevalence as world populations *** tremendous progress has been made,our understanding of the mechanisms that underlie the development of these diseases remains far from com *** troubling,despite the growing emotional and financial toll being to ken by neurodegenerative disorders,existing treatment options are limited almost exclusively to those that help manage symptoms but that lack the ability to alter the progression of the disease(Liu et al.,2022).

关键词： degenerative diseases

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Uncovering the dominant contribution of intermediate volatility compounds in secondary organic aerosol formation from biomass-burning emissions

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National science Review 2024年第3期11卷 150-158页

作者： Kun Li Jun Zhang David M.Bell Tiantian Wang Houssni Lamkaddam Tianqu Cui Lu Qi Mihnea surdu Dongyu Wang Lin Du Imad El Haddad jay G.slowik Andre s.H.Prevot Environment Research Institute Shandong University Laboratory of Atmospheric Chemistry Paul Scherrer Institute

Organic vapors from biomass burning are a major source of secondary organic aerosols(sOAs). Previous smog chamber studies found that the sOA contributors in biomass-burning emissions are mainly volatile organic compounds(VOCs). While intermediate volatility organic compounds(IVOCs) are efficient sOA precursors and contribute a considerable fraction of biomass-burning emissions, their contribution to sOA formation has not been directly observed. Here, by deploying a newly-developed oxidation flow reactor to study sOA formation from wood burning, we find that IVOCs can contribute ～70% of the formed sOA, i.e.>2 times more than VOCs. This previously missing sOA fraction is interpreted to be due to the high wall losses of semi-volatile oxidation products of IVOCs in smog chambers. The finding in this study reveals that sOA production from biomass burning is much higher than previously thought, and highlights the urgent need for more research on the IVOCs from biomass burning and potentially other emission sources.

关键词： atmospheric chemistry biomass burning secondary organic aerosol intermediate volatility organic compounds

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Neuropsychological detection and prediction using machine learning algorithms: a comprehensive review

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智慧医学（英文） 2024年第3期04卷 177-187页

作者： shah Manan shandilya Ananya Patel Kirtan Mehta Manya sanghavi jay Pandya Aum Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gandhinagar Gujarat India Walt Whitman High school Bethesda Maryland USA st. Kabir school Ahmedabad Gujarat India Anand Niketan school Ahmedabad Gujarat India Department of Computer Engineering School of Technology Pandit Deendayal Energy University Gandhinagar Gujarat India

Neuropsychological disorders (e.g., dementia, epilepsy, brain cancer, autism, stroke, and multiple sclerosis) adversely affect the quality of life of patients and their families;moreover, in some instances, they may lead to loss of life. The primary aim was to evaluate and compare the use of machine learning in neuropsychological research in contrast to traditional approaches such as through case studies. This was achieved by referring to earlier studies on this subject. This article presented the use of support vector machines (sVMs) and convolutional neural networks (CNN) for detecting and predicting neuropsychological diseases, such as dementia and Alzheimer’s disease. Challenges in using these models include data availability, quality, variability, model interpretability, and validation. Experimental findings have demonstrated the potential of these models in this field. It has been shown that sVM models are robust and efficient in processing and classifying data, particularly in neuroimaging applications, such as magnetic resonance imaging (MRI). CNNs have excelled in handling visual input;thus, they have been used in neuroimaging segregation, recognition, and classification, with applications in brain tumor segmentation, radiation therapy, robotic neurosurgery, and disease prediction. Future research will explore asymmetric differences among left- and right-handed patients, incorporate longitudinal studies, and utilize larger sample sizes. The use of machine learning models has the potential to revolutionize the diagnosis and treatment of neuropsychological diseases, allowing for early detection and intervention. This approach could offer significant advantages to healthcare, such as cost-effective diagnosis and treatment, to help save lives and preserve the quality of life of patients.

关键词： Machine learning Neuropsychological support vector machine Convolution neural network

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创造吧,我们的星球

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疯狂英语（新悦读） 2024年第3期 41-41,75页

作者： jay sivanesan Paranam 不详

让我们居住的星球变得更好,你我都可贡献一分力量!Max Han is helping to bring about environmental changes through YUFE(Youths United for Earth). When he was 19, Han co-founded the grassroots organization with his friend, ... 详细信息

关键词： planet roots helping

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