Pub Date : 2024-11-07DOI: 10.1016/j.isci.2024.111334
Taotao Zhao , Tong Zhang , Zijun Tao , Zhe Zhou , Xiaofeng Xia , Zhengjun Wu , Feiyi Wang , Jun Ren , Erfei Wang
Lysosomal biothiols play critical roles in numerous cellular processes and diseases. Researching an effective method for real-time labeling biothiols in lysosomes is of great significance and urgency, as it could provide essential information for the diagnosis of relevant diseases. In this study, we developed a lysosome-targeted fluorescent probe (LY-DCM-P) with a large Stokes shift of 150 nm for the sensitive and selective detection of biothiols in vivo and in vitro. Additionally, LY-DCM-P showed low cytotoxicity and excellent lysosome-targeted ability. The probe was successfully employed to monitor fluctuations in lysosomal biothiols in various living systems, enabling enormous potential to accurately monitor the occurrence and progress of biothiol-related diseases.
{"title":"A lysosome-targeted fluorescent probe with large Stokes shift for visualizing biothiols in vivo and in vitro","authors":"Taotao Zhao , Tong Zhang , Zijun Tao , Zhe Zhou , Xiaofeng Xia , Zhengjun Wu , Feiyi Wang , Jun Ren , Erfei Wang","doi":"10.1016/j.isci.2024.111334","DOIUrl":"10.1016/j.isci.2024.111334","url":null,"abstract":"<div><div>Lysosomal biothiols play critical roles in numerous cellular processes and diseases. Researching an effective method for real-time labeling biothiols in lysosomes is of great significance and urgency, as it could provide essential information for the diagnosis of relevant diseases. In this study, we developed a lysosome-targeted fluorescent probe (LY-DCM-P) with a large Stokes shift of 150 nm for the sensitive and selective detection of biothiols <em>in vivo</em> and <em>in vitro</em>. Additionally, LY-DCM-P showed low cytotoxicity and excellent lysosome-targeted ability. The probe was successfully employed to monitor fluctuations in lysosomal biothiols in various living systems, enabling enormous potential to accurately monitor the occurrence and progress of biothiol-related diseases.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"27 12","pages":"Article 111334"},"PeriodicalIF":4.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.isci.2024.111321
Simone Romeni , Daniela De Luca , Luca Pierantoni , Laura Toni , Gabriele Marino , Sara Moccia , Silvestro Micera
Retinal stimulation (RS) allows restoring vision in blind patients, but it covers only a narrow region of the visual field. Optic nerve stimulation (ONS) has the potential to produce visual perceptions spanning the whole visual field, but it produces very irregular phosphenes. We introduced a geometrical model converting retinal and optic nerve firing rates into visual perceptions and vice versa and a method to estimate the best perceptions elicitable through an electrode configuration. We then compared in silico ONS and RS through simulated prosthetic vision of static and dynamic visual scenes. Both simulations and SPV experiments showed that it might be possible to reconstruct natural visual scenes with ONS and RS, and that ONS wide field-of-view allows the perception of more detail in dynamic scenarios than RS. Our findings suggest that ONS could represent an interesting approach for vision restoration and that our model can be used to optimize it.
视网膜刺激（RS）可以让失明患者恢复视力，但它只能覆盖视野的一个狭窄区域。视神经刺激（ONS）有可能产生跨越整个视野的视觉感知，但它产生的幻视非常不规则。我们引入了一个几何模型，将视网膜和视神经的发射率转换为视觉感知，反之亦然。然后，我们通过对静态和动态视觉场景的模拟义眼，比较了硅ONS和RS。模拟和 SPV 实验都表明，使用 ONS 和 RS 有可能重建自然视觉场景，而且 ONS 的宽视场比 RS 能够感知动态场景中更多的细节。我们的研究结果表明，ONS 可能是一种有趣的视觉恢复方法，我们的模型可用于对其进行优化。
{"title":"A computational model to design wide field-of-view optic nerve neuroprostheses","authors":"Simone Romeni , Daniela De Luca , Luca Pierantoni , Laura Toni , Gabriele Marino , Sara Moccia , Silvestro Micera","doi":"10.1016/j.isci.2024.111321","DOIUrl":"10.1016/j.isci.2024.111321","url":null,"abstract":"<div><div>Retinal stimulation (RS) allows restoring vision in blind patients, but it covers only a narrow region of the visual field. Optic nerve stimulation (ONS) has the potential to produce visual perceptions spanning the whole visual field, but it produces very irregular phosphenes. We introduced a geometrical model converting retinal and optic nerve firing rates into visual perceptions and vice versa and a method to estimate the best perceptions elicitable through an electrode configuration. We then compared in silico ONS and RS through simulated prosthetic vision of static and dynamic visual scenes. Both simulations and SPV experiments showed that it might be possible to reconstruct natural visual scenes with ONS and RS, and that ONS wide field-of-view allows the perception of more detail in dynamic scenarios than RS. Our findings suggest that ONS could represent an interesting approach for vision restoration and that our model can be used to optimize it.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"27 12","pages":"Article 111321"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.isci.2024.111260
Surendran Parvathy , Budhaditya Basu , Suresh Surya , Rahul Jose , Vadakkath Meera , Paul Ann Riya , Nair Pradeep Jyothi , Rajendran Sanalkumar , Viviane Praz , Nicolò Riggi , Biju Surendran Nair , Kamalesh K. Gulia , Mukesh Kumar , Balachandran Krishnamma Binukumar , Jackson James
Tlx3, a master regulator of the fate specification of excitatory neurons, is primarily known to function in post-mitotic cells. Although we have previously identified TLX3 expression in the proliferating granule neuron progenitors (GNPs) of cerebellum, its primary role is unknown. Here, we demonstrate that the dysfunction of Tlx3 from the GNPs significantly reduced its proliferation through regulating anti-proliferative genes. Consequently, the altered generation of GNPs resulted in cerebellar hypoplasia, patterning defects, granule neuron-Purkinje ratio imbalance, and aberrant synaptic connections in the cerebellum. This altered cerebellar homeostasis manifested into a typical autism-like behavior in mice with motor, and social function disabilities. We also show the presence of TLX3 variants with uncharacterized mutations in human cases of autism spectrum disorder (ASD). Altogether, our study establishes Tlx3 as a critical gene involved in developing GNPs and that its deletion from the early developmental stage culminates in autism.
{"title":"TLX3 regulates CGN progenitor proliferation during cerebellum development and its dysfunction can lead to autism","authors":"Surendran Parvathy , Budhaditya Basu , Suresh Surya , Rahul Jose , Vadakkath Meera , Paul Ann Riya , Nair Pradeep Jyothi , Rajendran Sanalkumar , Viviane Praz , Nicolò Riggi , Biju Surendran Nair , Kamalesh K. Gulia , Mukesh Kumar , Balachandran Krishnamma Binukumar , Jackson James","doi":"10.1016/j.isci.2024.111260","DOIUrl":"10.1016/j.isci.2024.111260","url":null,"abstract":"<div><div><em>Tlx3</em>, a master regulator of the fate specification of excitatory neurons, is primarily known to function in post-mitotic cells. Although we have previously identified TLX3 expression in the proliferating granule neuron progenitors (GNPs) of cerebellum, its primary role is unknown. Here, we demonstrate that the dysfunction of <em>Tlx3</em> from the GNPs significantly reduced its proliferation through regulating anti-proliferative genes. Consequently, the altered generation of GNPs resulted in cerebellar hypoplasia, patterning defects, granule neuron-Purkinje ratio imbalance, and aberrant synaptic connections in the cerebellum. This altered cerebellar homeostasis manifested into a typical autism-like behavior in mice with motor, and social function disabilities. We also show the presence of <em>TLX3</em> variants with uncharacterized mutations in human cases of autism spectrum disorder (ASD). Altogether, our study establishes <em>Tlx3</em> as a critical gene involved in developing GNPs and that its deletion from the early developmental stage culminates in autism.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"27 12","pages":"Article 111260"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.isci.2024.111306
Hongjie Tang , Lingji Kong , Zheng Fang , Zutao Zhang , Jianhong Zhou , Hongyu Chen , Jiantong Sun , Xiaolong Zou
As rail transit continues to develop, expanding railway networks increase the demand for sustainable energy supply and intelligent infrastructure management. In recent years, advanced rail self-powered technology has rapidly progressed toward artificial intelligence and the internet of things (AIoT). This review primarily discusses the self-powered and self-sensing systems in rail transit, analyzing their current characteristics and innovative potentials in different scenarios. Based on this analysis, we further explore an IoT framework supported by sustainable self-powered sensing systems including device nodes, network communication, and platform deployment. Additionally, technologies about cloud computing and edge computing deployed in railway IoT enable more effective utilization. The deployed intelligent algorithms such as machine learning (ML) and deep learning (DL) can provide comprehensive monitoring, management, and maintenance in railway environments. Furthermore, this study explores research in other cross-disciplinary fields to investigate the potential of emerging technologies and analyze the trends for future development in rail transit.
{"title":"Sustainable and smart rail transit based on advanced self-powered sensing technology","authors":"Hongjie Tang , Lingji Kong , Zheng Fang , Zutao Zhang , Jianhong Zhou , Hongyu Chen , Jiantong Sun , Xiaolong Zou","doi":"10.1016/j.isci.2024.111306","DOIUrl":"10.1016/j.isci.2024.111306","url":null,"abstract":"<div><div>As rail transit continues to develop, expanding railway networks increase the demand for sustainable energy supply and intelligent infrastructure management. In recent years, advanced rail self-powered technology has rapidly progressed toward artificial intelligence and the internet of things (AIoT). This review primarily discusses the self-powered and self-sensing systems in rail transit, analyzing their current characteristics and innovative potentials in different scenarios. Based on this analysis, we further explore an IoT framework supported by sustainable self-powered sensing systems including device nodes, network communication, and platform deployment. Additionally, technologies about cloud computing and edge computing deployed in railway IoT enable more effective utilization. The deployed intelligent algorithms such as machine learning (ML) and deep learning (DL) can provide comprehensive monitoring, management, and maintenance in railway environments. Furthermore, this study explores research in other cross-disciplinary fields to investigate the potential of emerging technologies and analyze the trends for future development in rail transit.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"27 12","pages":"Article 111306"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.isci.2024.111325
Yeongju Jung , Seung Hwan Ko
As sustainable thermal management becomes a global priority, the development of radiative cooling (RC) technology has recently emerged as a promising solution. Simultaneously, recent advent of artificial intelligence (AI) offers the potential to revolutionize current research in sustainable cooling strategies. This article discusses the advancement of radiative cooling technology through the integration of AI, tackling the challenging issues arising from the conventional approach and offering strategic solutions to address global issues. AI, capable of mimicking or exceeding human capabilities through various algorithms, enables the efficient optimization of RC structures. Moreover, integrating AI with advanced RC technologies, which have the potential to surpass traditional RC configurations and applications but are still in the early stages, can further accelerate progress in the field of RC. Hence, AI-driven RC technologies will contribute to addressing the increasingly prevalent environmental challenges, further being a leading solution for next-generation sustainable thermal managements as these technologies continue to mature.
{"title":"Radiative cooling technology with artificial intelligence","authors":"Yeongju Jung , Seung Hwan Ko","doi":"10.1016/j.isci.2024.111325","DOIUrl":"10.1016/j.isci.2024.111325","url":null,"abstract":"<div><div>As sustainable thermal management becomes a global priority, the development of radiative cooling (RC) technology has recently emerged as a promising solution. Simultaneously, recent advent of artificial intelligence (AI) offers the potential to revolutionize current research in sustainable cooling strategies. This article discusses the advancement of radiative cooling technology through the integration of AI, tackling the challenging issues arising from the conventional approach and offering strategic solutions to address global issues. AI, capable of mimicking or exceeding human capabilities through various algorithms, enables the efficient optimization of RC structures. Moreover, integrating AI with advanced RC technologies, which have the potential to surpass traditional RC configurations and applications but are still in the early stages, can further accelerate progress in the field of RC. Hence, AI-driven RC technologies will contribute to addressing the increasingly prevalent environmental challenges, further being a leading solution for next-generation sustainable thermal managements as these technologies continue to mature.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"27 12","pages":"Article 111325"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.isci.2024.111322
Alexandra Manchel , Michelle Gee , Rajanikanth Vadigepalli
As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be assembled to generate multi-organ systems pathophysiological models. Finally, we discuss how generic multi-organ models can be brought to the patient-specific level thus permitting their use in the clinical setting.
{"title":"From sampling to simulating: Single-cell multiomics in systems pathophysiological modeling","authors":"Alexandra Manchel , Michelle Gee , Rajanikanth Vadigepalli","doi":"10.1016/j.isci.2024.111322","DOIUrl":"10.1016/j.isci.2024.111322","url":null,"abstract":"<div><div>As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be assembled to generate multi-organ systems pathophysiological models. Finally, we discuss how generic multi-organ models can be brought to the patient-specific level thus permitting their use in the clinical setting.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"27 12","pages":"Article 111322"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-02DOI: 10.1016/j.isci.2024.111313
Yuyang Sha , Qingyue Zhang , Xiaobing Zhai , Menghui Hou , Jingtao Lu , Weiyu Meng , Yuefei Wang , Kefeng Li , Jing Ma
Cervical lesions pose a significant threat to women’s health worldwide. Colposcopy is essential for screening and treating cervical lesions, but its effectiveness depends on the doctor’s experience. Artificial intelligence-based solutions via colposcopy images have shown great potential in cervical lesions screening. However, some challenges still need to be addressed, such as low algorithm performance and lack of high-quality multi-modal datasets. Here, we established a multi-modal colposcopy dataset of 2,273 HPV+ patients, comprising original colposcopy images, acetic acid reactions at 60s and 120s, iodine staining, diagnostic reports, and pathological results. Utilizing this dataset, we developed CerviFusionNet, a hybrid architecture that merges convolutional neural networks and vision transformers to learn robust representations. We designed a temporal module to capture dynamic changes in acetic acid sequences, which can boost the model performance without sacrificing inference speed. Compared with several existing methods, CerviFusionNet demonstrated excellent accuracy and efficiency.
{"title":"CerviFusionNet: A multi-modal, hybrid CNN-transformer-GRU model for enhanced cervical lesion multi-classification","authors":"Yuyang Sha , Qingyue Zhang , Xiaobing Zhai , Menghui Hou , Jingtao Lu , Weiyu Meng , Yuefei Wang , Kefeng Li , Jing Ma","doi":"10.1016/j.isci.2024.111313","DOIUrl":"10.1016/j.isci.2024.111313","url":null,"abstract":"<div><div>Cervical lesions pose a significant threat to women’s health worldwide. Colposcopy is essential for screening and treating cervical lesions, but its effectiveness depends on the doctor’s experience. Artificial intelligence-based solutions via colposcopy images have shown great potential in cervical lesions screening. However, some challenges still need to be addressed, such as low algorithm performance and lack of high-quality multi-modal datasets. Here, we established a multi-modal colposcopy dataset of 2,273 HPV+ patients, comprising original colposcopy images, acetic acid reactions at 60s and 120s, iodine staining, diagnostic reports, and pathological results. Utilizing this dataset, we developed CerviFusionNet, a hybrid architecture that merges convolutional neural networks and vision transformers to learn robust representations. We designed a temporal module to capture dynamic changes in acetic acid sequences, which can boost the model performance without sacrificing inference speed. Compared with several existing methods, CerviFusionNet demonstrated excellent accuracy and efficiency.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"27 12","pages":"Article 111313"},"PeriodicalIF":4.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-02DOI: 10.1016/j.isci.2024.111131
Hui Tang , Jia-yuan Zhong , Xiang-tian Yu , Hua Chai , Rui Liu , Tao Zeng
There is an urgent need to understand the molecular landscape beyond the conventional cellular landscape, maximizing the translational use and generalized interpretation of state-of-the-art single-cell genomic techniques in biological studies. We introduced a multimodal explainable artificial intelligence (xAI) model Vec3D to identify a joint definition of cellular states and their distribution in a quantified graphic organization as structured molecular landscape (SML). First, Vec3D substantially improves the accuracy and efficiency of multimodal data analysis. Further, an SML was learned on CITE-seq data of human peripheral blood mononuclear cells (PBMCs), simultaneously revealing the predictive multi-label cell state and corresponding joint cell state markers with complementary effects from genes and proteins. Third, Vec3D demonstrated that the spatial-temporal SML efficiently characterizes molecular dynamics of cell lineages during human lung development. Collectively, Vec3D will be a broadly applicable computational method in the principle of “AI-for-biology”, providing a unified framework for understanding cellular homeostasis and imbalance through SML dynamics.
{"title":"Exploring structured molecular landscape from single-cell multi-omics data by an explainable multimodal model","authors":"Hui Tang , Jia-yuan Zhong , Xiang-tian Yu , Hua Chai , Rui Liu , Tao Zeng","doi":"10.1016/j.isci.2024.111131","DOIUrl":"10.1016/j.isci.2024.111131","url":null,"abstract":"<div><div>There is an urgent need to understand the molecular landscape beyond the conventional cellular landscape, maximizing the translational use and generalized interpretation of state-of-the-art single-cell genomic techniques in biological studies. We introduced a multimodal explainable artificial intelligence (xAI) model Vec3D to identify a joint definition of cellular states and their distribution in a quantified graphic organization as structured molecular landscape (SML). First, Vec3D substantially improves the accuracy and efficiency of multimodal data analysis. Further, an SML was learned on CITE-seq data of human peripheral blood mononuclear cells (PBMCs), simultaneously revealing the predictive multi-label cell state and corresponding joint cell state markers with complementary effects from genes and proteins. Third, Vec3D demonstrated that the spatial-temporal SML efficiently characterizes molecular dynamics of cell lineages during human lung development. Collectively, Vec3D will be a broadly applicable computational method in the principle of “AI-for-biology”, providing a unified framework for understanding cellular homeostasis and imbalance through SML dynamics.</div></div>","PeriodicalId":342,"journal":{"name":"iScience","volume":"27 12","pages":"Article 111131"},"PeriodicalIF":4.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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