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1.
Mutations in the lamin A/C gene cause the rare genetic disorder Hutchinson-Gilford progeria syndrome (HGPS). The prevalent mutation results in the production of a mutant lamin A protein with an internal 50 amino acid deletion which causes a cellular aging phenotype characterized by growth defects, limited replicative lifespan, and nuclear membrane abnormalities. However, the relevance of these findings to normal human aging is unclear. In this study, we demonstrate that increased levels of wild-type lamin A in normal human cells result in decreased replicative lifespan and nuclear membrane abnormalities that lead to apoptotic cell death and senescence in a manner that is strongly reminiscent of the phenotype shown by HGPS cells. In contrast to the accelerated aging defects observed in HGPS cells, the progeroid phenotype resulting from increased expression of wild-type lamin A can be rescued by overexpression of ZMPSTE24, the metalloproteinase responsible for the removal of the farnesylated carboxyl terminal region of lamin A. Furthermore, farnesyltransferase inhibitors also serve to reverse the progeroid phenotype resulting from increased lamin A expression. Significantly, cells expressing elevated levels of lamin A display abnormal lamin A localization and similar alterations in the nuclear distribution of lamin A are also observed in cells from old-age individuals. These data demonstrate that the metabolism of wild-type lamin A is delicately poised and even in the absence of disease-linked mutations small perturbations in this system are sufficient to cause prominent nuclear defects and result in a progeroid phenotype. 相似文献
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Kevin G. Phillips Sandra M. Baker-Groberg Owen J.T. McCarty 《Journal of visualized experiments : JoVE》2014,(86)
We describe the use of a standard optical microscope to perform quantitative measurements of mass, volume, and density on cellular specimens through a combination of bright field and differential interference contrast imagery. Two primary approaches are presented: noninterferometric quantitative phase microscopy (NIQPM), to perform measurements of total cell mass and subcellular density distribution, and Hilbert transform differential interference contrast microscopy (HTDIC) to determine volume. NIQPM is based on a simplified model of wave propagation, termed the paraxial approximation, with three underlying assumptions: low numerical aperture (NA) illumination, weak scattering, and weak absorption of light by the specimen. Fortunately, unstained cellular specimens satisfy these assumptions and low NA illumination is easily achieved on commercial microscopes. HTDIC is used to obtain volumetric information from through-focus DIC imagery under high NA illumination conditions. High NA illumination enables enhanced sectioning of the specimen along the optical axis. Hilbert transform processing on the DIC image stacks greatly enhances edge detection algorithms for localization of the specimen borders in three dimensions by separating the gray values of the specimen intensity from those of the background. The primary advantages of NIQPM and HTDIC lay in their technological accessibility using “off-the-shelf” microscopes. There are two basic limitations of these methods: slow z-stack acquisition time on commercial scopes currently abrogates the investigation of phenomena faster than 1 frame/minute, and secondly, diffraction effects restrict the utility of NIQPM and HTDIC to objects from 0.2 up to 10 (NIQPM) and 20 (HTDIC) μm in diameter, respectively. Hence, the specimen and its associated time dynamics of interest must meet certain size and temporal constraints to enable the use of these methods. Excitingly, most fixed cellular specimens are readily investigated with these methods. 相似文献
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Gabriel L. Zanellato;Gabriel A. Pagnossin;Mauricio Failla;Juan F. Masello; 《Ecology and evolution》2024,14(8):e70172
Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, counts are time-consuming, particularly if carried out manually. Here, we took advantage of convolutional neural networks (CNN) to count the total number of nest-entrances in 222 photographs covering the largest known Psittaciformes (Aves) colony in the world. We conducted our study at the largest Burrowing Parrot Cyanoliseus patagonus colony, located on a cliff facing the Atlantic Ocean in the vicinity of El Cóndor village, in north-eastern Patagonia, Argentina. We also aimed to investigate the distribution of nest-entrances along the cliff with the colony. For this, we used three CNN architectures, U-Net, ResUnet, and DeepLabv3. The U-Net architecture showed the best performance, counting a mean of 59,842 Burrowing Parrot nest-entrances across the colony, with a mean absolute error of 2.7 nest-entrances over the testing patches, measured as the difference between actual and predicted counts per patch. Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest-entrances along the cliff. We show that the strong changes observed in the distribution of nest-entrances are a measurable effect of a long record of human-induced disturbance to the Burrowing Parrot colony at El Cóndor. Given the paramount importance of the Burrowing Parrot colony at El Cóndor, which concentrates 71% of the world's population of this species, we advocate that it is imperative to reduce such a degree of disturbance before the parrots reach the limit of their capacity of adaptation. 相似文献
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To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory (LSTM), with diverse input datasets, and compares their performance. The Blast_Weather_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data. 相似文献
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Xiaodong Mu Chieh Tseng William S. Hambright Polina Matre Chih‐Yi Lin Palas Chanda Wanqun Chen Jianhua Gu Sudheer Ravuri Yan Cui Ling Zhong John P. Cooke Laura J. Niedernhofer Paul D. Robbins Johnny Huard 《Aging cell》2020,19(8)
Hutchinson–Gilford progeria syndrome (HGPS) is caused by the accumulation of mutant prelamin A (progerin) in the nuclear lamina, resulting in increased nuclear stiffness and abnormal nuclear architecture. Nuclear mechanics are tightly coupled to cytoskeletal mechanics via lamin A/C. However, the role of cytoskeletal/nuclear mechanical properties in mediating cellular senescence and the relationship between cytoskeletal stiffness, nuclear abnormalities, and senescent phenotypes remain largely unknown. Here, using muscle‐derived mesenchymal stromal/stem cells (MSCs) from the Zmpste24?/? (Z24?/?) mouse (a model for HGPS) and human HGPS fibroblasts, we investigated the mechanical mechanism of progerin‐induced cellular senescence, involving the role and interaction of mechanical sensors RhoA and Sun1/2 in regulating F‐actin cytoskeleton stiffness, nuclear blebbing, micronuclei formation, and the innate immune response. We observed that increased cytoskeletal stiffness and RhoA activation in progeria cells were directly coupled with increased nuclear blebbing, Sun2 expression, and micronuclei‐induced cGAS‐Sting activation, part of the innate immune response. Expression of constitutively active RhoA promoted, while the inhibition of RhoA/ROCK reduced cytoskeletal stiffness, Sun2 expression, the innate immune response, and cellular senescence. Silencing of Sun2 expression by siRNA also repressed RhoA activation, cytoskeletal stiffness and cellular senescence. Treatment of Zmpste24?/? mice with a RhoA inhibitor repressed cellular senescence and improved muscle regeneration. These results reveal novel mechanical roles and correlation of cytoskeletal/nuclear stiffness, RhoA, Sun2, and the innate immune response in promoting aging and cellular senescence in HGPS progeria. 相似文献
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随着世界人口的不断增长、食物需求量的不断增加,以及气候的不断变化,如何提高农作物产量已成为人类面临的一个巨大挑战。传统设计育种耗时长、效率低,已经不能满足新时代的育种需求。随着基因型和表型数据成本的不断降低,以及各种组学数据的爆炸式增长,人工智能技术作为能够在大数据中高效率挖掘信息的工具,在生物学领域受到了广泛关注。人工智能指导的设计育种将大大加快育种的效率,给育种带来革命性的变化。介绍了人工智能特别是深度学习在作物基因组学和遗传改良中的应用,并进行了总结与展望,以期为智能设计育种提供新的思路。 相似文献
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衰老相关新基因CSIG的cDNA克隆和功能 总被引：2，自引：0，他引：2
为了获得 2BS细胞衰老过程中表达下降的差异基因片段Y6 2的编码序列 ,以cDNA末端快速扩增法获得细胞衰老相关新基因CSIG(cellularsenescenceinhibitedgene ,细胞衰老抑制基因 )的cDNA全长 .CSIGcDNA长 196 1bp ,编码 4 90个氨基酸 ,在多种重要组织中都有不同程度的表达 ;蛋白产物位于细胞核内特定位点 ,可能在核仁中聚集 .细胞转染表明 :CSIG可抑制细胞衰老并延长细胞寿限 ,可能通过核糖体生物合成过程或基因转录调节来调控细胞衰老过程 相似文献
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《Structure (London, England : 1993)》2022,30(6):900-908.e2
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Arlindo L. Oliveira 《Biotechnology journal》2019,14(8)
Developments in biotechnology are increasingly dependent on the extensive use of big data, generated by modern high‐throughput instrumentation technologies, and stored in thousands of databases, public and private. Future developments in this area depend, critically, on the ability of biotechnology researchers to master the skills required to effectively integrate their own contributions with the large amounts of information available in these databases. This article offers a perspective of the relations that exist between the fields of big data and biotechnology, including the related technologies of artificial intelligence and machine learning and describes how data integration, data exploitation, and process optimization correspond to three essential steps in any future biotechnology project. The article also lists a number of application areas where the ability to use big data will become a key factor, including drug discovery, drug recycling, drug safety, functional and structural genomics, proteomics, pharmacogenetics, and pharmacogenomics, among others. 相似文献
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蛋白质是有机生命体内不可或缺的化合物,在生命活动中发挥着多种重要作用,了解蛋白质的功能有助于医学和药物研发等领域的研究。此外,酶在绿色合成中的应用一直备受人们关注,但是由于酶的种类和功能多种多样,获取特定功能酶的成本高昂,限制了其进一步的应用。目前,蛋白质的具体功能主要通过实验表征确定,该方法实验工作繁琐且耗时耗力,同时,随着生物信息学和测序技术的高速发展,已测序得到的蛋白质序列数量远大于功能获得注释的序列数量,高效预测蛋白质功能变得至关重要。随着计算机技术的蓬勃发展,由数据驱动的机器学习方法已成为应对这些挑战的有效解决方案。本文对蛋白质功能及其注释方法以及机器学习的发展历程和操作流程进行了概述,聚焦于机器学习在酶功能预测领域的应用,对未来人工智能辅助蛋白质功能高效研究的发展方向提出了展望。 相似文献
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随着组学技术的不断发展,对于不同层次和类型的生物数据的获取方法日益成熟。在疾病诊治过程中会产生大量数据,通过机器学习等人工智能方法解析复杂、多维、多尺度的疾病大数据,构建临床决策支持工具,辅助医生寻找快速且有效的疾病诊疗方案是非常必要的。在此过程中,机器学习等人工智能方法的选择显得尤为重要。基于此,本文首先从类型和算法角度对临床决策支持领域中常用的机器学习等方法进行简要综述,分别介绍了支持向量机、逻辑回归、聚类算法、Bagging、随机森林和深度学习,对机器学习等方法在临床决策支持中的应用做了相应总结和分类,并对它们的优势和不足分别进行讨论和阐述,为临床决策支持中机器学习等人工智能方法的选择提供有效参考。 相似文献
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Jarrett Blair Michael D. Weiser Michael Kaspari Matthew Miller Cameron Siler Katie E. Marshall 《Ecology and evolution》2020,10(23):13143
- Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large‐scale insect identification projects are time‐consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.
- Here, we outline a methodology for training classification models to identify pitfall trap‐collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single‐level) identifying ground beetles from the species to subfamily level. All models were trained using pre‐extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.
- The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single‐level classification method at higher taxonomic levels.
- The general methodology outlined here serves as a proof‐of‐concept for classifying pitfall trap‐collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large‐scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa.
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Feng Cheng Chenggui Wang Yufei Ji Biao Yang Jiawei Shu Kesi Shi Lulu Wang Shaoke Wang Yuang Zhang Xianpeng Huang Xiaopeng Zhou Kaishun Xia Chengzhen Liang Qixin Chen Fangcai Li 《Aging cell》2022,21(4)
Rejuvenation of nucleus pulposus cells (NPCs) in degenerative discs can reverse intervertebral disc degeneration (IDD). Partial reprogramming is used to rejuvenate aging cells and ameliorate progression of aging tissue to avoiding formation of tumors by classical reprogramming. Understanding the effects and potential mechanisms of partial reprogramming in degenerative discs provides insights for development of new therapies for IDD treatment. The findings of the present study show that partial reprogramming through short‐term cyclic expression of Oct‐3/4, Sox2, Klf4, and c‐Myc (OSKM) inhibits progression of IDD, and significantly reduces senescence related phenotypes in aging NPCs. Mechanistically, short‐term induction of OSKM in aging NPCs activates energy metabolism as a “energy switch” by upregulating expression of Hexokinase 2 (HK2) ultimately promoting redistribution of cytoskeleton and restoring the aging state in aging NPCs. These findings indicate that partial reprogramming through short‐term induction of OSKM has high therapeutic potential in the treatment of IDD. 相似文献
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Cai Chen;Shu-Le Li;Qing-Lin Chen;Manuel Delgado-Baquerizo;Zhao-Feng Guo;Fenghua Wang;Yao-Yang Xu;Yong-Guan Zhu; 《Global Change Biology》2024,30(8):e17466
Global patterns in soil microbiomes are driven by non-linear environmental thresholds. Fertilization is known to shape the soil microbiome of terrestrial ecosystems worldwide. Yet, whether fertilization influences global thresholds in soil microbiomes remains virtually unknown. Here, utilizing optimized machine learning models with Shapley additive explanations on a dataset of 10,907 soil samples from 24 countries, we discovered that the microbial community response to fertilization is highly dependent on environmental contexts. Furthermore, the interactions among nitrogen (N) addition, pH, and mean annual temperature contribute to non-linear patterns in soil bacterial diversity. Specifically, we observed positive responses within a soil pH range of 5.2–6.6, with the influence of higher temperature (>15°C) on bacterial diversity being positive within this pH range but reversed in more acidic or alkaline soils. Additionally, we revealed the threshold effect of soil organic carbon and total nitrogen, demonstrating how temperature and N addition amount interacted with microbial communities within specific edaphic concentration ranges. Our findings underscore how complex environmental interactions control soil bacterial diversity under fertilization. 相似文献
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Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few. Although, skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms including supervised, unsupervised, and reinforcement learning along with examples. Second, we discuss the application of ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare such as system privacy and ethical concerns and provide suggestions for future applications. 相似文献