Applications in Plant Sciences最新文献_第7页

From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2 从叶子到标签：使用LeafMachine2构建用于快速植物标本分析的模块化机器学习网络。

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-10-16 DOI: 10.1002/aps3.11548

William N. Weaver, Stephen A. Smith

Premise

Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.

Methods

LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms.

Results

LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types.

Discussion

LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non-archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.

前提：植物数量性状在生物学研究中起着至关重要的作用。然而，传统的植物形态测量方法耗时且可扩展性有限。我们介绍了LeafMachine2，这是一套模块化的机器学习和计算机视觉工具，可以从数字植物数据集中自动提取一组基本的叶片特征。方法：对LeafMachine2进行494766个人工注释的训练，这些注释来自288个机构的5648张植物标本馆图像，代表2663个物种；它采用一套植物成分检测和分割算法来分离单个叶片、叶柄、果实、花朵、木材样本、芽和根。我们的陆地标记网络自动识别和测量出现在大多数阔叶分类群上的九个伪地标。文本标签和条形码由档案组件检测器自动识别，并为光学字符识别方法或自然语言处理算法做好准备。结果：LeafMachine2可以从至少245个被子植物科中提取性状数据，并计算26种常用标尺类型的像素-度量转换因子。讨论：LeafMachine2是一种高效的工具，可以生成大量的植物特征数据，甚至可以从遮挡或重叠的叶子、田间图像和非档案数据集中生成。我们的项目以及类似的举措，在消除植物标本馆植物性状数据采集的瓶颈方面取得了重大进展，并将重点转移到数据修订和质量控制的关键任务上。

{"title":"From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2","authors":"William N. Weaver, Stephen A. Smith","doi":"10.1002/aps3.11548","DOIUrl":"10.1002/aps3.11548","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non-archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71432179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Rapid imaging in the field followed by photogrammetry digitally captures the otherwise lost dimensions of plant specimens 在野外进行快速成像，然后进行摄影测量，以数字方式捕捉植物标本丢失的尺寸。

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-10-16 DOI: 10.1002/aps3.11547

Nicole James, Alex Adkinson, Austin Mast

Premise

We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three-dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting.

Methods and Results

We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional.

Conclusions

The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it.

前提：我们认识到，为了三维（3D）建模的目的，在采集植物标本时需要一个定制的成像协议，并且缺乏一个广泛适用的摄影测量协议，该协议涵盖了植物标本几何形状的异质性和枯萎等过程带来的挑战。方法和结果：我们制定了一份设备清单和一套详细的协议，描述了如何在植物标本变形之前（例如，通过按压）在现场捕捉植物标本的图像，以及如何在Agisoft Metashape Professional中从图像集生成3D模型。结论：设备清单和协议是可以对样本几何形状超出了所考虑的六种类型的范围，对于那些以前没有使用过它的人来说，这是一个很容易进入摄影测量的方法。

{"title":"Rapid imaging in the field followed by photogrammetry digitally captures the otherwise lost dimensions of plant specimens","authors":"Nicole James, Alex Adkinson, Austin Mast","doi":"10.1002/aps3.11547","DOIUrl":"10.1002/aps3.11547","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three-dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A pipeline for the rapid collection of color data from photographs 用于从照片中快速收集颜色数据的管道。

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-10-06 DOI: 10.1002/aps3.11546

Yvonne Luong, Ariel Gasca-Herrera, Tracy M. Misiewicz, Benjamin E. Carter

Abstract Premise There are relatively few studies of flower color at landscape scales that can address the relative importance of competing mechanisms (e.g., biotic: pollinators; abiotic: ultraviolet radiation, drought stress) at landscape scales. Methods We developed an R shiny pipeline to sample color from images that were automatically downloaded using query results from a search using iNaturalist or the Global Biodiversity Information Facility (GBIF). The pipeline was used to sample ca. 4800 North American wallflower (Erysimum, Brassicaceae) images from iNaturalist. We tested whether flower color was distributed non‐randomly across the landscape and whether spatial patterns were correlated with climate. We also used images including ColorCheckers to compare analyses of raw images to color‐calibrated images. Results Flower color was strongly non‐randomly distributed spatially, but did not correlate strongly with climate, with most of the variation explained instead by spatial autocorrelation. However, finer‐scale patterns including local correlations between elevation and color were observed. Analyses using color‐calibrated and raw images revealed similar results. Discussion This pipeline provides users the ability to rapidly capture color data from iNaturalist images and can be a useful tool in detecting spatial or temporal changes in color using citizen science data.

前提：在景观尺度上对花朵颜色的研究相对较少，可以解决景观尺度上竞争机制（例如，生物：传粉者；非生物：紫外线辐射、干旱胁迫）的相对重要性。方法：我们开发了一个R闪亮管道，从使用iNaturalist或全球生物多样性信息设施（GBIF）搜索的查询结果自动下载的图像中采样颜色。该管道用于对iNaturalist的约4800张北美壁花（Erysimum，Brassicaceae）图像进行采样。我们测试了花朵的颜色是否在整个景观中非随机分布，以及空间模式是否与气候相关。我们还使用包括ColorCheckers在内的图像将原始图像的分析与颜色校准图像进行比较。结果：花的颜色在空间上具有强烈的非随机分布，但与气候的相关性不强，大部分变化由空间自相关解释。然而，观察到了更精细的尺度模式，包括海拔和颜色之间的局部相关性。使用颜色校准和原始图像的分析显示了类似的结果。讨论：该管道为用户提供了从iNaturalist图像中快速捕捉颜色数据的能力，并且可以成为使用公民科学数据检测颜色的空间或时间变化的有用工具。

{"title":"A pipeline for the rapid collection of color data from photographs","authors":"Yvonne Luong, Ariel Gasca-Herrera, Tracy M. Misiewicz, Benjamin E. Carter","doi":"10.1002/aps3.11546","DOIUrl":"10.1002/aps3.11546","url":null,"abstract":"Abstract Premise There are relatively few studies of flower color at landscape scales that can address the relative importance of competing mechanisms (e.g., biotic: pollinators; abiotic: ultraviolet radiation, drought stress) at landscape scales. Methods We developed an R shiny pipeline to sample color from images that were automatically downloaded using query results from a search using iNaturalist or the Global Biodiversity Information Facility (GBIF). The pipeline was used to sample ca. 4800 North American wallflower (Erysimum, Brassicaceae) images from iNaturalist. We tested whether flower color was distributed non‐randomly across the landscape and whether spatial patterns were correlated with climate. We also used images including ColorCheckers to compare analyses of raw images to color‐calibrated images. Results Flower color was strongly non‐randomly distributed spatially, but did not correlate strongly with climate, with most of the variation explained instead by spatial autocorrelation. However, finer‐scale patterns including local correlations between elevation and color were observed. Analyses using color‐calibrated and raw images revealed similar results. Discussion This pipeline provides users the ability to rapidly capture color data from iNaturalist images and can be a useful tool in detecting spatial or temporal changes in color using citizen science data.","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes FieldPrism：一个使用摄影测量标记和二维码从现场图像创建快照凭证的系统。

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-09-28 DOI: 10.1002/aps3.11545

William N. Weaver, Stephen A. Smith

Premise

Field images are important sources of information for research in the natural sciences. However, images that lack photogrammetric scale bars, including most iNaturalist observations, cannot yield accurate trait measurements. We introduce FieldPrism, a novel system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers.

Methods and Results

Our photogrammetric background templates (FieldSheets) increase the utility of field images by providing machine-readable scale bars and photogrammetric reference points to automatically correct image distortion and calculate a pixel-to-metric conversion ratio. Users can generate a QR code flipbook derived from a specimen identifier naming hierarchy, enabling machine-readable specimen identification for automatic file renaming. We also developed FieldStation, a Raspberry Pi–based mobile imaging apparatus that records images, GPS location, and metadata redundantly on up to four USB storage devices and can be monitored and controlled from any Wi-Fi connected device.

Conclusions

FieldPrism is a flexible software tool designed to standardize and improve the utility of images captured in the field. When paired with the optional FieldStation, researchers can create a self-contained mobile imaging apparatus for quantitative trait data collection.

前提：野外图像是自然科学研究的重要信息来源。然而，缺乏摄影测量比例尺的图像，包括大多数自然学家的观察，无法产生准确的特征测量。我们介绍了FieldPrism，这是一个由摄影测量标记、二维码和软件组成的新型系统，用于自动管理快照凭证。方法和结果：我们的摄影测量背景模板（FieldSheets）通过提供机器可读的比例尺和摄影测量参考点来自动校正图像失真并计算像素与度量的转换率，从而提高了现场图像的实用性。用户可以从标本标识符命名层次结构中生成二维码挂图，从而实现机器可读的标本识别，实现文件自动重命名。我们还开发了FieldStation，这是一款基于Raspberry Pi的移动成像设备，可在多达四个USB存储设备上冗余记录图像、GPS位置和元数据，并可通过任何Wi-Fi连接设备进行监控。结论：FieldPrism是一种灵活的软件工具，旨在标准化和提高现场拍摄图像的实用性。当与可选的FieldStation配对时，研究人员可以创建一个独立的移动成像设备，用于定量特征数据收集。

{"title":"FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes","authors":"William N. Weaver, Stephen A. Smith","doi":"10.1002/aps3.11545","DOIUrl":"10.1002/aps3.11545","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Field images are important sources of information for research in the natural sciences. However, images that lack photogrammetric scale bars, including most iNaturalist observations, cannot yield accurate trait measurements. We introduce FieldPrism, a novel system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>Our photogrammetric background templates (FieldSheets) increase the utility of field images by providing machine-readable scale bars and photogrammetric reference points to automatically correct image distortion and calculate a pixel-to-metric conversion ratio. Users can generate a QR code flipbook derived from a specimen identifier naming hierarchy, enabling machine-readable specimen identification for automatic file renaming. We also developed FieldStation, a Raspberry Pi–based mobile imaging apparatus that records images, GPS location, and metadata redundantly on up to four USB storage devices and can be monitored and controlled from any Wi-Fi connected device.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>FieldPrism is a flexible software tool designed to standardize and improve the utility of images captured in the field. When paired with the optional FieldStation, researchers can create a self-contained mobile imaging apparatus for quantitative trait data collection.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A maceration technique for soft plant tissue without hazardous chemicals 一种不含有害化学物质的植物软组织浸渍技术。

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-09-18 DOI: 10.1002/aps3.11543

Phillip C. Klahs, Elizabeth K. McMurchie, Jordan J. Nikkel, Lynn G. Clark

Abstract Premise Current methods for maceration of plant tissue use hazardous chemicals. The new method described here improves the safety of dissection and maceration of soft plant tissues for microscopic imaging by using the harmless enzyme pectinase. Methods and Results Leaf material from a variety of land plants was obtained from living plants and dried herbarium specimens. Concentrations of aqueous pectinase and soaking schedules were optimized, and tissues were manually dissected while submerged in fresh solution following a soaking period. Most leaves required 2–4 h of soaking; however, delicate leaves could be macerated after 30 min while tougher leaves required 12 h to 3 days of soaking. Staining techniques can also be used with this method, and permanent or semi‐permanent slides can be prepared. The epidermis, vascular tissue, and individual cells were imaged at magnifications of 10× to 400×. Only basic safety precautions were needed. Conclusions This pectinase method is a cost‐effective and safe way to obtain images of epidermal peels, separated tissues, or isolated cells from a wide range of plant taxa.

前提：目前植物组织的浸渍方法使用危险化学品。本文所述的新方法通过使用无害的酶果胶酶提高了对软植物组织进行显微成像的解剖和浸渍的安全性。方法和结果：从活体植物和植物标本馆干燥标本中提取了多种陆地植物的叶片材料。优化了果胶酶水溶液的浓度和浸泡时间表，并在浸泡一段时间后将组织浸入新鲜溶液中时手动解剖。大多数叶片需要2-4片浸泡h；然而，娇嫩的叶子可以在30分钟后浸渍分钟，而需要更硬的叶子12 浸泡h至3天。染色技术也可以与这种方法一起使用，并且可以制备永久或半永久载玻片。表皮、血管组织和单个细胞在10倍至400倍的放大倍数下成像。只需要采取基本的安全预防措施。结论：这种果胶酶方法是一种成本效益高且安全的方法，可以从多种植物类群中获得表皮皮、分离组织或分离细胞的图像。

{"title":"A maceration technique for soft plant tissue without hazardous chemicals","authors":"Phillip C. Klahs, Elizabeth K. McMurchie, Jordan J. Nikkel, Lynn G. Clark","doi":"10.1002/aps3.11543","DOIUrl":"10.1002/aps3.11543","url":null,"abstract":"Abstract Premise Current methods for maceration of plant tissue use hazardous chemicals. The new method described here improves the safety of dissection and maceration of soft plant tissues for microscopic imaging by using the harmless enzyme pectinase. Methods and Results Leaf material from a variety of land plants was obtained from living plants and dried herbarium specimens. Concentrations of aqueous pectinase and soaking schedules were optimized, and tissues were manually dissected while submerged in fresh solution following a soaking period. Most leaves required 2–4 h of soaking; however, delicate leaves could be macerated after 30 min while tougher leaves required 12 h to 3 days of soaking. Staining techniques can also be used with this method, and permanent or semi‐permanent slides can be prepared. The epidermis, vascular tissue, and individual cells were imaged at magnifications of 10× to 400×. Only basic safety precautions were needed. Conclusions This pectinase method is a cost‐effective and safe way to obtain images of epidermal peels, separated tissues, or isolated cells from a wide range of plant taxa.","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Spatially resolved detection of small molecules from press-dried plant tissue using MALDI imaging 使用MALDI成像对来自压制干燥的植物组织的小分子进行空间分辨检测。

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-09-11 DOI: 10.1002/aps3.11539

Zane G. Long, Jonathan V. Le, Benjamin B. Katz, Belen G. Lopez, Emily D. Tenenbaum, Bonnie Semmling, Ryan J. Schmidt, Felix Grün, Carter T. Butts, Rachel W. Martin

Premise

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a chemical imaging method that can visualize spatial distributions of particular molecules. Plant tissue imaging has so far mostly used cryosectioning, which can be impractical for the preparation of large-area imaging samples, such as full flower petals. Imaging unsectioned plant tissue presents its own difficulties in extracting metabolites to the surface due to the waxy cuticle.

Methods

We address this by using established delipidation techniques combined with a solvent vapor extraction prior to applying the matrix with many low-concentration sprays.

Results

Using this procedure, we imaged tissue from three different plant species (two flowers and one carnivorous plant leaf). Material factorization analysis of the resulting data reveals a wide range of plant-specific small molecules with varying degrees of localization to specific portions of the tissue samples, while facilitating detection and removal of signal from background sources.

Conclusions

This work demonstrates applicability of MALDI-MSI to press-dried plant samples without freezing or cryosectioning, setting the stage for spatially resolved molecule identification. Increased mass resolution and inclusion of tandem mass spectrometry are necessary next steps to allow more specific and reliable compound identification.

前提：基质辅助激光解吸/电离质谱成像（MALDI-MSI）是一种可以可视化特定分子空间分布的化学成像方法。到目前为止，植物组织成像主要使用冷冻切片，这对于制备大面积成像样本（如完整的花瓣）来说是不切实际的。由于角质层蜡质，未切片的植物组织在提取代谢产物到表面方面存在困难。方法：在使用许多低浓度喷雾施用基质之前，我们通过使用已建立的脱脂技术和溶剂蒸汽萃取来解决这一问题。结果：使用该程序，我们对三种不同植物（两朵花和一片食肉植物叶子）的组织进行了成像。对所得数据的材料因子分解分析揭示了广泛的植物特异性小分子，它们对组织样本的特定部分具有不同程度的定位，同时有助于检测和去除背景源的信号。结论：这项工作证明了MALDI-MSI在不冷冻或冷冻切片的情况下适用于压制干燥的植物样品，为空间分辨分子鉴定奠定了基础。提高质量分辨率和纳入串联质谱法是下一步进行更具体和可靠的化合物鉴定所必需的。

{"title":"Spatially resolved detection of small molecules from press-dried plant tissue using MALDI imaging","authors":"Zane G. Long, Jonathan V. Le, Benjamin B. Katz, Belen G. Lopez, Emily D. Tenenbaum, Bonnie Semmling, Ryan J. Schmidt, Felix Grün, Carter T. Butts, Rachel W. Martin","doi":"10.1002/aps3.11539","DOIUrl":"10.1002/aps3.11539","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a chemical imaging method that can visualize spatial distributions of particular molecules. Plant tissue imaging has so far mostly used cryosectioning, which can be impractical for the preparation of large-area imaging samples, such as full flower petals. Imaging unsectioned plant tissue presents its own difficulties in extracting metabolites to the surface due to the waxy cuticle.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We address this by using established delipidation techniques combined with a solvent vapor extraction prior to applying the matrix with many low-concentration sprays.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Using this procedure, we imaged tissue from three different plant species (two flowers and one carnivorous plant leaf). Material factorization analysis of the resulting data reveals a wide range of plant-specific small molecules with varying degrees of localization to specific portions of the tissue samples, while facilitating detection and removal of signal from background sources.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This work demonstrates applicability of MALDI-MSI to press-dried plant samples without freezing or cryosectioning, setting the stage for spatially resolved molecule identification. Increased mass resolution and inclusion of tandem mass spectrometry are necessary next steps to allow more specific and reliable compound identification.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 5","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71419884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A comparison of methods for excluding light from stems to evaluate stem photosynthesis 叶片叶片光合作用测定方法的比较

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-09-04 DOI: 10.1002/aps3.11542

Nadia A. Valverdi, Camilla Acosta, Gabriella R. Dauber, Gregory R. Goldsmith, Eleinis Ávila-Lovera

Premise

A comparison of methods using different materials to exclude light from stems to prevent stem CO₂ exchange (i.e., photosynthesis), without affecting stem conductance to water vapor, surface temperature, and relative humidity, was conducted on stems of avocado trees in California.

Methods and Results

The experiment featured three materials: aluminum foil, paper-based wrap, and mineral-based paint. We examined stem CO₂ exchange with and without the light exclusion treatments. We also examined stem surface temperature, relative humidity, and photosynthetic active radiation (PAR) under the cover materials. All materials reduced PAR and stem CO₂ exchange. However, aluminum foil reduced stem surface temperature and increased relative humidity.

Conclusions

Methods used to study stem CO₂ exchange through light exclusion have historically relied on methods that may induce experimental artifacts. Among the methods tested here, mineral-based paint effectively reduced PAR without affecting stem surface temperature and relative humidity around the stem.

在加利福尼亚的牛油果树的茎上进行了一项比较，使用不同的材料来排除茎上的光，以防止茎上的二氧化碳交换(即光合作用)，而不影响茎对水蒸气的电导率、表面温度和相对湿度。实验采用了三种材料:铝箔、纸基包装和矿物基涂料。我们研究了不排除光处理和排除光处理下茎秆的CO2交换。我们还检测了覆盖材料下茎表面温度、相对湿度和光合有效辐射(PAR)。所有材料都降低了PAR并阻止了CO2交换。然而，铝箔降低了茎表面温度，增加了相对湿度。历史上，通过光排斥来研究干CO2交换的方法依赖于可能诱发实验伪影的方法。在这里测试的方法中，矿物基涂料有效地降低了PAR，而不会影响茎干周围的表面温度和相对湿度。

{"title":"A comparison of methods for excluding light from stems to evaluate stem photosynthesis","authors":"Nadia A. Valverdi, Camilla Acosta, Gabriella R. Dauber, Gregory R. Goldsmith, Eleinis Ávila-Lovera","doi":"10.1002/aps3.11542","DOIUrl":"10.1002/aps3.11542","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>A comparison of methods using different materials to exclude light from stems to prevent stem CO<sub>2</sub> exchange (i.e., photosynthesis), without affecting stem conductance to water vapor, surface temperature, and relative humidity, was conducted on stems of avocado trees in California.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>The experiment featured three materials: aluminum foil, paper-based wrap, and mineral-based paint. We examined stem CO<sub>2</sub> exchange with and without the light exclusion treatments. We also examined stem surface temperature, relative humidity, and photosynthetic active radiation (PAR) under the cover materials. All materials reduced PAR and stem CO<sub>2</sub> exchange. However, aluminum foil reduced stem surface temperature and increased relative humidity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Methods used to study stem CO<sub>2</sub> exchange through light exclusion have historically relied on methods that may induce experimental artifacts. Among the methods tested here, mineral-based paint effectively reduced PAR without affecting stem surface temperature and relative humidity around the stem.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 6","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43884912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data 对GOgetter的更正:用于总结和可视化植物遗传数据的GO精简注释的管道

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-09-02 DOI: 10.1002/aps3.11544

Sessa, E. B., R. R. Masalia, N. Arrigo, M. S. Barker, and J. A. Pelosi. 2023. GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data. Applications in Plant Sciences 11(4): e11536.

In the Acknowledgments, a grant number was left out of the sentence “Funding was provided by the National Science Foundation (DEB #1844930 to E.B.S.).” This should have read “Funding was provided by the National Science Foundation (DEB #1844930 and IOS #2310485 to E.B.S.).”

We apologize for this error.

[这更正了文章DOI:10.1002/aps3.11536.]。

引用次数: 0

RootBot: High-throughput root stress phenotyping robot RootBot：高通量根系应力表型机器人

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-08-28 DOI: 10.1002/aps3.11541

Mia Ruppel, Sven K. Nelson, Grace Sidberry, Madison Mitchell, Daniel Kick, Shawn K. Thomas, Katherine E. Guill, Melvin J. Oliver, Jacob D. Washburn

Premise

Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil.

Methods and Results

RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data.

Conclusions

The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.

全球气温升高导致干旱的频率和严重程度增加。在农业作物中，这导致产量下降、经济损失和超市食品成本增加。干燥土壤中的根系生长维持对植物在干旱下的生存和表现能力起着重要作用，但由于根系在土壤下，表型根系生长极为困难。RootBot是一种自动化的高通量表型机器人，它消除了许多困难，并减少了对主根进行干旱胁迫研究所需的时间。RootBot使用透明板模拟根系生长条件，创建一个填充土壤和聚乙二醇（PEG）的间隙，以模拟低土壤湿度。RootBot有一个带有垂直槽的龙门系统来固定透明板，理论上可以一次评估50多块板。还选择、开发、测试并广泛改进了软件管道，用于运行RootBot成像过程、存储和组织图像以及分析和提取数据。RootBot平台及其设计和测试的经验教训为更好地理解根系的耐旱机制以及确定作物的育种和基因工程目标提供了宝贵的资源。

{"title":"RootBot: High-throughput root stress phenotyping robot","authors":"Mia Ruppel, Sven K. Nelson, Grace Sidberry, Madison Mitchell, Daniel Kick, Shawn K. Thomas, Katherine E. Guill, Melvin J. Oliver, Jacob D. Washburn","doi":"10.1002/aps3.11541","DOIUrl":"10.1002/aps3.11541","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Premise</h3>\u0000 \u0000 <p>Higher temperatures across the globe are causing an increase in the frequency and severity of droughts. In agricultural crops, this results in reduced yields, financial losses, and increased food costs at the supermarket. Root growth maintenance in drying soils plays a major role in a plant's ability to survive and perform under drought, but phenotyping root growth is extremely difficult due to roots being under the soil.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>RootBot is an automated high-throughput phenotyping robot that eliminates many of the difficulties and reduces the time required for performing drought-stress studies on primary roots. RootBot simulates root growth conditions using transparent plates to create a gap that is filled with soil and polyethylene glycol (PEG) to simulate low soil moisture. RootBot has a gantry system with vertical slots to hold the transparent plates, which theoretically allows for evaluating more than 50 plates at a time. Software pipelines were also co-opted, developed, tested, and extensively refined for running the RootBot imaging process, storing and organizing the images, and analyzing and extracting data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The RootBot platform and the lessons learned from its design and testing represent a valuable resource for better understanding drought tolerance mechanisms in roots, as well as for identifying breeding and genetic engineering targets for crop plants.</p>\u0000 </section>\u0000 </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 6","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bsapubs.onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.11541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41711925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Correction to “A comparison of freezer-stored DNA and herbarium tissue samples for chloroplast assembly and genome skimming” 对“用于叶绿体组装和基因组脱脂的冷冻储存DNA和植物标本组织样本的比较”的更正

IF 3.6 3区生物学 Q2 PLANT SCIENCES

Applications in Plant Sciences

Pub Date : 2023-08-19 DOI: 10.1002/aps3.11540

McAssey, E. V., Downs, C., Yorkston, M., Morden, C., and Heyduk, K. 2023. A comparison of freezer-stored DNA and herbarium tissue samples for chloroplast assembly and genome skimming. Applications in Plant Sciences 11(3): e11527

A statistical error was found after article publication. The relevant text from the Results section is provided below, with the corrected values shown in bold text. The error does not affect the findings of the study.

“Herbarium tissue library samples had significantly smaller insert sizes of mapped chloroplast reads compared to their freezer-stored DNA paired samples, taking into account covariates of read numbers and year (F_1,25 = 229.243, P < 0.001). There was also a significant interaction effect between library size and sampling year (F_1,25 = 9.753, P < 0.01). Similarly, herbarium tissue samples also had higher amounts of adapter sequences in the reads (F_1,25 = 85.009, P < 0.001), with sampling year a significant covariate in the model (F_1,25 = 6.378, P < 0.05).”

We apologize for this error.

mccassey, e.v.， Downs, C, Yorkston, M, Morden, C, and Heyduk, K. 2023。用于叶绿体组装和基因组脱脂的冷冻储存DNA和植物标本组织样本的比较。植物科学应用11(3):e11527文章发表后发现统计误差。结果部分的相关文本如下所示，更正后的值以粗体显示。这个错误不影响研究的结果。考虑到读取数和年份的协变量(F1,25 = 229.243, P < 0.001)，植物标本馆组织文库样本的叶绿体图谱插入尺寸明显小于冷冻保存的DNA配对样本。文库规模与采样年份之间也存在显著的交互效应(F1,25 = 9.753, P < 0.01)。同样，植物标本组织样本在reads中也有较高数量的适配器序列(F1,25 = 85.009, P < 0.001)，采样年份在模型中是一个显著的协变量(F1,25 = 6.378, P < 0.05)。我们为这个错误道歉。

{"title":"Correction to “A comparison of freezer-stored DNA and herbarium tissue samples for chloroplast assembly and genome skimming”","authors":"","doi":"10.1002/aps3.11540","DOIUrl":"10.1002/aps3.11540","url":null,"abstract":"<p>McAssey, E. V., Downs, C., Yorkston, M., Morden, C., and Heyduk, K. 2023. A comparison of freezer-stored DNA and herbarium tissue samples for chloroplast assembly and genome skimming. <i>Applications in Plant Sciences</i> 11(3): e11527</p><p>A statistical error was found after article publication. The relevant text from the Results section is provided below, with the corrected values shown in bold text. The error does not affect the findings of the study.</p><p>“Herbarium tissue library samples had significantly smaller insert sizes of mapped chloroplast reads compared to their freezer-stored DNA paired samples, taking into account covariates of read numbers and year (<b><i>F</i><sub>1,25</sub> = 229.243</b>, <i><b>P</b></i> < <b>0.001</b>). There was also a significant interaction effect between library size and sampling year (<b><i>F</i><sub>1,25</sub> = 9.753</b>, <i>P</i> < 0.01). Similarly, herbarium tissue samples also had higher amounts of adapter sequences in the reads (<b><i>F</i><sub>1,25</sub> = 85.009</b>, <i>P</i> < 0.001), with sampling year a significant covariate in the model (<b><i>F</i><sub>1,25</sub> = 6.378</b>, <i>P</i> < 0.05).”</p><p>We apologize for this error.</p>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"11 4","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10439819/pdf/APS3-11-e11540.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10034571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1