Search claims · PlantClaims

Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

Environmental inputs were crucial for correct inference of the studied network structures.

Taxon: Arabidopsis ThalianaArabidopsis thalianae1
Outcome: correctly inferring network structurese0
Comparator: environmental inputse0

Verbatim

e0
“environmental inputs are crucial for correctly inferring network structures”
✓ located verbatim in the source · abstract, characters 1186–1261

e1
“flowering genes of the model plant Arabidopsis Thaliana”
✓ located verbatim in the source · abstract, characters 917–972

Title

Impact of environmental inputs on reverse-engineering approach to network structures

Jianhua Wu; James L Sinfield; Vicky Buchanan-Wollaston; et al. BMC Systems Biology 2009. doi:10.1186/1752-0509-3-113

Other claims from this paper (2)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

A network method constructed empirical gene expression relational structures.

Taxon: maizeZea mayst0
Outcome: empirical gene expression relational structurese0

Not reportedComparator

Verbatim

e0
“A network-based method was also explored to construct the empirical gene expression relational structures”
✓ located verbatim in the source · abstract, characters 639–744

t0
“maize”
✓ located verbatim in the source · title, characters 117–122

Title

Relating significance and relations of differentially expressed genes in response to Aspergillus flavus infection in maize

Matthew C. Asters; W. Paul Williams; Andy D. Perkins; et al. Scientific Reports 2014. doi:10.1038/srep04815

Other claims from this paper (2)

Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

The harmonic causal method detected environmental inputs and uncovered network structures, especially for periodically oscillating biological data.

Taxon: Arabidopsis ThalianaArabidopsis thalianae1
Outcome: detect environment inputs and uncover network structurese0
Qualifier: especially when the biological data exhibit periodic oscillationse0

Not reportedComparatorValue

Verbatim

e0
“Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations”
✓ located verbatim in the source · abstract, characters 1263–1449

e1
“flowering genes of the model plant Arabidopsis Thaliana”
✓ located verbatim in the source · abstract, characters 917–972

Title

Impact of environmental inputs on reverse-engineering approach to network structures

Jianhua Wu; James L Sinfield; Vicky Buchanan-Wollaston; et al. BMC Systems Biology 2009. doi:10.1186/1752-0509-3-113

Other claims from this paper (2)

Method / resource The harmonic causal method was successfully applied to Arabidopsis flowering gene microarray data.
Method / resource Environmental inputs were crucial for correct inference of the studied network structures.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

A network method constructed empirical maize gene-expression relational structures.

Taxon: maizeZea mayse1
Outcome: empirical gene expression relational structurese0

Not reportedComparatorValue

Verbatim

e0
“A network-based method was also explored to construct the empirical gene expression relational structures.”
✓ located verbatim in the source · abstract, characters 639–745

e1
“maize candidate genes based on the empirical gene expression data obtained by RT-qPCR technique from maize inbred lines”
✓ located verbatim in the source · abstract, characters 365–484

Title

Формирование спроса на социальные услуги

Наталья Сергеевна Михайлова Регион Экономика и Социология 2007. doi:10.1038/srep04815

Other claims from this paper (2)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Different convolutional-network structures were compared for recognition using carrot seedling images.

Taxon: carrotDaucus carotae0
Organ / stage: seedling periode0
Outcome: recognition resultse0
Comparator: different structurese0

Verbatim

e0
“Based on the datasets collected during the carrot seedling period, the convolutional neural networks with different structures were used to compare the recognition results and evaluate them”
✓ located verbatim in the source · abstract, characters 494–683

Title

Crop Weed Identification System Based on Convolutional Neural Network

Fengiuan Miao; Siqi Zheng; Bairui Tao 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT) 2019. doi:10.1109/iceict.2019.8846268

Other claims from this paper (1)

Method / resource The system was described as applicable to classification of other weeds and crops.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

An automated image-based network framework was developed to accurately segment and quantify actin structures and Golgi transport.

Outcome: segment and quantify actin cytoskeletal structures and Golgi transporte0

Not reportedTaxonComparatorDirection

Verbatim

e0
“Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport.”
✓ located verbatim in the source · abstract, characters 321–476

e1
“cytoplasmic streaming in plant cells”
✓ located verbatim in the source · abstract, characters 113–149

Title

System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells

David Breuer; Jacqueline Nowak; Alexander Ivakov; et al. Proceedings of the National Academy of Sciences 2017. doi:10.1073/pnas.1706711114

Other claims from this paper (5)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The neural network was able to detect other plant tissue structures such as vascular bundles and stone cell clusters.

Outcome: detect other plant tissue structures such as vascular bundles and stone cell clusterse1

Not reportedTaxonComparator

Verbatim

e1
“the neural network was able to detect other plant tissue structures such as vascular bundles and stone cell clusters (brachysclereids)”
✓ located verbatim in the source · abstract, characters 1109–1243

Title

Automatic 3D cell segmentation of fruit parenchyma tissue from X-ray micro CT images using deep learning

Leen Van Doorselaer; Pieter Verboven; Bart Nicolai Plant Methods 2024. doi:10.1186/s13007-024-01137-y

Other claims from this paper (6)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The MLP network with two hidden layers gave the best results among the ANN structures, based on the statistical criteria.

Taxon: apple slicet0
Material: MLP network with two hidden layerse3
Outcome: best resultse3
Qualifier: based on the statistical criteriae3
Comparator: Among different ANN structurese3

Verbatim

t0
“apple slice”
✓ located verbatim in the source · title, characters 100–111

e3
“Among different ANN structures, the best results were obtained for the MLP network with two hidden layers based on the statistical criteria”
✓ located verbatim in the source · abstract, characters 836–975

Title

A comparative study on the efficiency of two modeling approaches for predicting moisture content of apple slice during drying

Mohsen Dalvi‐Isfahan Journal of Food Process Engineering 2020. doi:10.1111/jfpe.13527

Other claims from this paper (3)

Method / resource The ANN model had better goodness of fit than the theoretical model for apple slice moisture content.
Method / resource The theoretical model agreed well with the experimental results for apple slice moisture content.
Method / resource Both the theoretical and the ANN approaches could reasonably forecast apple slice moisture content during drying.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The DC-DPCA module can be embedded into many network structures with minimal time and memory cost.

Taxon: crope0
Treatment: DC-DPCA modulee2
Outcome: time cost and memory coste2
Qualifier: minimale2

Not reportedComparator

Verbatim

e0
“Crop disease seriously affects food security and causes huge economic losses”
✓ located verbatim in the source · abstract, characters 0–76

e2
“the DC-DPCA module can be easily embedded into a variety of network structures with minimal time cost and memory cost”
✓ located verbatim in the source · abstract, characters 1746–1863

Title

Classification of Fine-Grained Crop Disease by Dilated Convolution and Improved Channel Attention Module

Xiang Zhang; Huiyi Gao; Li Wan Agriculture 2022. doi:10.3390/agriculture12101727

Other claims from this paper (4)

Method / resource ResNet50 with the DC-DPCA module reached 87.14% accuracy, above the original network at 85.38%.
Method / resource VGG16 with the DC-DPCA module reached 86.26% accuracy, above the original network at 83.22%.
Method / resource MobileNetV2 with the DC-DPCA module reached 86.24% accuracy, above the original network at 83.85%.
Method / resource InceptionV3 with the DC-DPCA module reached 86.77% accuracy, above the original network at 84.60%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The algorithm identified trait correlation structures and link patterns in matrices.

Taxon: chili pepperCapsicumt0
Outcome: Correlation structures and link patternse0
Comparator: Fruchterman-Reingold algorithme0

Verbatim

e0
“Correlation structures and link patterns can be easily identified in the matrices using the Fruchterman-Reingold algorithm with correlation network information.”
✓ located verbatim in the source · abstract, characters 322–482

t0
“chili pepper”
✓ located verbatim in the source · title, characters 72–84

Title

Correlation network analysis between phenotypic and genotypic traits of chili pepper

Anderson Rodrigo da Silva; Elizanilda Ramalho do Rêgo; Angela Maria dos Santos Pessoa; et al. Pesquisa Agropecuária Brasileira 2016. doi:10.1590/s0100-204x2016000400010

Other claims from this paper (3)

Observation Phenotypic and genotypic correlations shared the same fruit trait link pattern.
Observation Fruit traits had high broad sense heritability.
Association Leaf dimensions correlated with a cluster of fruit traits.

“network structures”

Claim types

Direction of the findings

Over time

Claim type

Research area

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Statistics