LIVIVO - Search results -

Search results

Result 1 - 10 of total 18

Search options

Article: Fast estimation of plant growth dynamics using deep neural networks.

Gall, Gabriella E C / Pereira, Talmo D / Jordan, Alex / Meroz, Yasmine

2022 Volume 18, Issue 1, Page(s) 21

Abstract: Background: In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic ... ...

Abstract	Background: In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results: Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling "skeleton" had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions: In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Language	English
Publishing date	2022-02-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2203723-8
ISSN	1746-4811
ISSN	1746-4811
DOI	10.1186/s13007-022-00851-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Article: Fast estimation of plant growth dynamics using deep neural networks

Gall, Gabriella E. C. / Pereira, Talmo D. / Jordan, Alex / Meroz, Yasmine

Plant methods. 2022 Dec., v. 18, no. 1

2022

Abstract: BACKGROUND: In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic ... ...

Abstract	BACKGROUND: In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). RESULTS: Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). CONCLUSIONS: In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Keywords	Arabidopsis ; automation ; gravity ; humans ; plant growth ; prediction ; skeleton
Language	English
Dates of publication	2022-12
Size	p. 21.
Publishing place	BioMed Central
Document type	Article
ZDB-ID	2203723-8
ISSN	1746-4811
ISSN	1746-4811
DOI	10.1186/s13007-022-00851-9
Database	NAL-Catalogue (AGRICOLA)

Order via subito

Article ; Online: Quantifying behavior to understand the brain.

Pereira, Talmo D / Shaevitz, Joshua W / Murthy, Mala

Nature neuroscience

2020 Volume 23, Issue 12, Page(s) 1537–1549

Abstract: Over the past years, numerous methods have emerged to automate the quantification of animal behavior at a resolution not previously imaginable. This has opened up a new field of computational ethology and will, in the near future, make it possible to ... ...

Abstract	Over the past years, numerous methods have emerged to automate the quantification of animal behavior at a resolution not previously imaginable. This has opened up a new field of computational ethology and will, in the near future, make it possible to quantify in near completeness what an animal is doing as it navigates its environment. The importance of improving the techniques with which we characterize behavior is reflected in the emerging recognition that understanding behavior is an essential (or even prerequisite) step to pursuing neuroscience questions. The use of these methods, however, is not limited to studying behavior in the wild or in strictly ethological settings. Modern tools for behavioral quantification can be applied to the full gamut of approaches that have historically been used to link brain to behavior, from psychophysics to cognitive tasks, augmenting those measurements with rich descriptions of how animals navigate those tasks. Here we review recent technical advances in quantifying behavior, particularly in methods for tracking animal motion and characterizing the structure of those dynamics. We discuss open challenges that remain for behavioral quantification and highlight promising future directions, with a strong emphasis on emerging approaches in deep learning, the core technology that has enabled the markedly rapid pace of progress of this field. We then discuss how quantitative descriptions of behavior can be leveraged to connect brain activity with animal movements, with the ultimate goal of resolving the relationship between neural circuits, cognitive processes and behavior.
MeSH term(s)	Animals ; Behavior/physiology ; Behavior, Animal/physiology ; Brain/physiology ; Humans ; Neurosciences
Language	English
Publishing date	2020-11-09
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Review
ZDB-ID	1420596-8
ISSN	1546-1726 ; 1097-6256
ISSN (online)	1546-1726
ISSN	1097-6256
DOI	10.1038/s41593-020-00734-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 4891: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 67: Show issues

Order via subito

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Open-source tools for behavioral video analysis: Setup, methods, and best practices.

Luxem, Kevin / Sun, Jennifer J / Bradley, Sean P / Krishnan, Keerthi / Yttri, Eric / Zimmermann, Jan / Pereira, Talmo D / Laubach, Mark

eLife

2023 Volume 12

Abstract: Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These ...

Abstract	Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional 'center of mass' tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.
MeSH term(s)	Animals ; Software ; Algorithms ; Behavior, Animal ; Ethology ; Video Recording
Language	English
Publishing date	2023-03-23
Publishing country	England
Document type	Review ; Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.79305
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: To Fight or Not to Fight.

Pereira, Talmo D / Murthy, Mala

Neuron

2017 Volume 95, Issue 5, Page(s) 986–988

Abstract: In this issue of Neuron, Watanabe et al. (2017) uncover how octopamine, an invertebrate norepinephrine analog, modulates the neural pathways that bias Drosophila males toward aggression. ...

Abstract	In this issue of Neuron, Watanabe et al. (2017) uncover how octopamine, an invertebrate norepinephrine analog, modulates the neural pathways that bias Drosophila males toward aggression.
MeSH term(s)	Aggression ; Animals ; Drosophila ; Drosophila Proteins ; Male ; Neurons ; Octopamine
Chemical Substances	Drosophila Proteins ; Octopamine (14O50WS8JD)
Language	English
Publishing date	2017-08-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	808167-0
ISSN	1097-4199 ; 0896-6273
ISSN (online)	1097-4199
ISSN	0896-6273
DOI	10.1016/j.neuron.2017.08.029
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

In stock of ZB MED Cologne/Königswinter

Zs.A 2496: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 92: Show issues

Order via subito

Details ▾

Article: A behavioral roadmap for the development of agency in the rodent.

Mitelut, Catalin / Diez Castro, Marielisa / Peterson, Ralph Emilio / Goncalves, Maria / Li, Jennifer / Gamer, Madeline Marie / Nilsson, Simon R O / Pereira, Talmo D / Sanes, Dan H

bioRxiv : the preprint server for biology

2024

Abstract: Behavioral interactions within the nuclear family play a pivotal role in the emergence of agency: the capacity to regulate physiological, psychological and social needs. While behaviors may develop over days or weeks in line with nervous system ... ...

Abstract	Behavioral interactions within the nuclear family play a pivotal role in the emergence of agency: the capacity to regulate physiological, psychological and social needs. While behaviors may develop over days or weeks in line with nervous system maturation, individual behaviors can occur on sub-second time scales making it challenging to track development in lab studies with brief observation periods, or in field studies with limited temporal precision and animal identification. Here we study development in families of gerbils, a highly social rodent, collecting tens of millions of behavior time points and implementing machine learning methods to track individual subjects. We provided maturing gerbils with a large, undisturbed environment between postnatal day 15 and the age at which they would typically disperse from the family unit (day 30). We identified complex and distinct developmental trajectories for food and water acquisition, solitary exploration, and social behaviors, some of which displayed sex differences and diurnal patterns. Our work supports the emergence of well-delineated autonomous and social behavior phenotypes that correlate with specific periods and loci of neural maturation.
Language	English
Publishing date	2024-04-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.10.566632
Database	MEDical Literature Analysis and Retrieval System OnLINE

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

Article: Social isolation recruits amygdala-cortical circuitry to escalate alcohol drinking.

Research square

2024

Abstract: How do social factors impact the brain and contribute to increased alcohol drinking? We found that social rank predicts alcohol drinking, where subordinates drink more than dominants. Furthermore, social isolation escalates alcohol drinking, particularly ...

Abstract	How do social factors impact the brain and contribute to increased alcohol drinking? We found that social rank predicts alcohol drinking, where subordinates drink more than dominants. Furthermore, social isolation escalates alcohol drinking, particularly impacting subordinates who display a greater increase in alcohol drinking compared to dominants. Using cellular resolution calcium imaging, we show that the basolateral amygdala-medial prefrontal cortex (BLA-mPFC) circuit predicts alcohol drinking in a rank-dependent manner, unlike non-specific BLA activity. The BLA-mPFC circuit becomes hyperexcitable during social isolation, detecting social isolation states. Mimicking the observed increases in BLA-mPFC activity using optogenetics was sufficient to increase alcohol drinking, suggesting the BLA-mPFC circuit may be a neural substrate for the negative impact of social isolation. To test the hypothesis that the BLA-mPFC circuit conveys a signal induced by social isolation to motivate alcohol consumption, we first determined if this circuit detects social information. Leveraging optogenetics in combination with calcium imaging and computer vision pose tracking, we found that BLA-mPFC circuitry governs social behavior and neural representation of social contact. We further show that BLA-mPFC stimulation mimics social isolation-induced mPFC encoding of sucrose and alcohol, and inhibition of the BLA-mPFC circuit decreases alcohol drinking following social isolation. Collectively, these data suggest the amygdala-cortical circuit mirrors a neural encoding state similar to social isolation and underlies social isolation-associated alcohol drinking.
Language	English
Publishing date	2024-03-21
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-4033115/v1
Database	MEDical Literature Analysis and Retrieval System OnLINE

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

Article ; Online: Fast and Efficient Root Phenotyping via Pose Estimation.

Plant phenomics (Washington, D.C.)

2024 Volume 6, Page(s) 175

Abstract: Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's ... ...

Abstract	Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (
Language	English
Publishing date	2024-04-12
Publishing country	United States
Document type	Journal Article
ISSN	2643-6515
ISSN (online)	2643-6515
DOI	10.34133/plantphenomics.0175
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Book ; Online: Open-Source Tools for Behavioral Video Analysis

Luxem, Kevin / Sun, Jennifer J. / Bradley, Sean P. / Krishnan, Keerthi / Yttri, Eric A. / Zimmermann, Jan / Pereira, Talmo D. / Laubach, Mark

Setup, Methods, and Development

2022

Abstract	Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional "center of mass" tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior. Comment: 26 pages, 2 figures, 3 tables; this is a commentary on video methods for analyzing behavior in animals that emerged from a working group organized by the OpenBehavior project (openbehavior.com)
Keywords	Quantitative Biology - Quantitative Methods ; Computer Science - Computer Vision and Pattern Recognition ; Quantitative Biology - Neurons and Cognition
Subject code	004
Publishing date	2022-04-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Fast and efficient root phenotyping via pose estimation.

bioRxiv : the preprint server for biology

2023

Abstract	Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (
Language	English
Publishing date	2023-11-21
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.20.567949
Database	MEDical Literature Analysis and Retrieval System OnLINE

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾

To top

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Inter-library loan at ZB MED