CC BY Research Articles

Agency under German Occupation: Conservationist Policy-Making in The Netherlands, 1940–1945

The paper provides a political history of nature conservation in the Netherlands during the Second World War, 1940–1945. Nature and nature conservation were not mere victims of Nazi German oppression or destruction; rather, conservationist organisations clearly had agency under difficult circumstances. The movement’s leaders were accustomed to using informal contacts, semi-formal arrangements and back channel deals with government authorities and agencies, and continued to exert influence on the new Department of Education, Science and Protection of Culture with considerable success after 1940. Although these consultation structures and the Department’s power to impose measures by decree could not always prevent severe damage, this story sheds new light on the history of the nature conservation movement as an historical actor in its own right under the conditions of war and occupation. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

Understanding and Mitigating Bias in Imaging Artificial Intelligence.

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.

Interview with Jiro Tanaka

This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

Three-Dimensional Ultrastructure of Arabidopsis Cotyledons Infected with Colletotrichum higginsianum.

We used serial block-face scanning electron microscopy (SBF-SEM) to study the host-pathogen interface between Arabidopsis cotyledons and the hemibiotrophic fungus Colletotrichum higginsianum. By combining high-pressure freezing and freeze-substitution with SBF-SEM, followed by segmentation and reconstruction of the imaging volume using the freely accessible software IMOD, we created 3D models of the series of cytological events that occur during the Colletotrichum-Arabidopsis susceptible interaction. We found that the host cell membranes underwent massive expansion to accommodate the rapidly growing intracellular hypha. As the fungal infection proceeded from the biotrophic to the necrotrophic stage, the host cell membranes went through increasing levels of disintegration culminating in host cell death. Intriguingly, we documented autophagosomes in proximity to biotrophic hyphae using transmission electron microscopy (TEM) and a concurrent increase in autophagic flux between early to mid/late biotrophic phase of the infection process. Occasionally, we observed osmiophilic bodies in the vicinity of biotrophic hyphae using TEM only and near necrotrophic hyphae under both TEM and SBF-SEM. Overall, we established a method for obtaining serial SBF-SEM images, each with a lateral (x-y) pixel resolution of 10 nm and an axial (z) resolution of 40 nm, that can be reconstructed into interactive 3D models using the IMOD. Application of this method to the Colletotrichum-Arabidopsis pathosystem allowed us to more fully understand the spatial arrangement and morphological architecture of the fungal hyphae after they penetrate epidermal cells of Arabidopsis cotyledons and the cytological changes the host cell undergoes as the infection progresses toward necrotrophy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Emplacing Eminescu: The Memorial Spatialization of Romania’s National Poet in Urban Street Nomenclature

Besides being canonized as Romania’s “national poet,” Mihai Eminescu was hailed as the “complete man of Romanian culture” and the “absolute Romanian,” among a profusion of encomiastic praises. Eminescu was immortalized in myriads of ways, from the most solemn and official (e.g. state-run rituals) to the most prosaic and banal (e.g. banknotes). In this article, I investigate the spatial dimension of Eminescu’s memory by charting the commemorative landscape defined by toponymy and public monuments honoring the national poet in Romania. Particular attention is given to Eminescu’s presence in urban street nomenclature and its regional distribution across the country. Drawing on several datasets of spatial information, I use statistical modeling techniques to examine the factors underpinning Eminescu’s memorial namescape. Employing logistic regression analyses, the article highlights an uneven geography of Eminescu’s memory and points out the variables structuring its particular spatial patterning. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

CYCLOPS: A cyclists’ orientation data acquisition system using RGB camera and inertial measurement units (IMU)

This paper introduces CYCLOPS, an acquisition system developed to capture images and inertial measurement data of moving cyclists from a vehicle. The development of CYCLOPS addresses the need to acquire useful data for training machine learning models capable of predicting the motion intentions of cyclists on urban roads. Considering its application, it is a completely original development. The system consists of two devices. The first device is installed on the bicycle and is based on an electronic acquisition board comprising an inertial measurement unit (IMU), a microcontroller, and a transceiver for sending the cyclist’s acceleration and orientation data to a vehicle. The second device is installed on the vehicle and uses the same board architecture to acquire the vehicle’s accelerations and orientations, along with an RGB monocular camera. The data is stored in real-time in a laptop’s drive for subsequent analysis and manipulation. The hardware architecture is presented in detail, including the designs to install the devices, for IMUs configuration, and software installation on the laptop. All design and software files required to develop the proposed system are available for download at: doi.org/10.17632/3yx5y8b7tm.1, licensed under the Open-source license CC BY 4.0.

SootImage: An image recreation, post-processing validation procedure for sooting axisymmetric flames

Validation is a vital part of any computational fluid dynamics study. Validation is done by comparing the computed results to the experimental measurements performed in a known configuration. In sooting flames, such a comparison is non-trivial since the measurements have a wide range of uncertainty due to difficulties in directly measuring soot volume fractions. This work introduces a different way to verify the computations using a software package called SootImage. In this proposed methodology, comparisons are not made directly to the computed properties of interest (soot volume fraction and temperature). Instead, a post-processing procedure is performed to obtain an image of the flame based on the computed properties. This reconstructed image is compared to an actual image of the flame being studied. The algorithm of the image reconstruction utilized within SootImage is presented in detail. Finally, the usage of SootImage is demonstrated on a co-flowing, laminar ethylene/air diffusion flame. Program summaryProgram Title: SootImageCPC Library link to program files:https://doi.org/10.17632/nc5myw64km.1Developer's repository link:https://github.com/VictorChernov/SootImageLicensing provisions: CC BY NC 3.0Programming language: MATLABNature of problem: The software allows comparing images of a real flame with the recreated image of a simulated flame. This requires two major steps. One is the recreation of the image of an axisymmetrical flame from the computed soot volume fraction and temperature fields for a known camera and optical configuration. The second is cropping and position images to obtain a meaningful comparison.Solution method: The solution recreates the image by integrating the soot thermal radiation over a line-of-sight. Soot is assumed to emit as gray body. The integrated radiation is passed through the color filters of the camera and is translated to pixel values. After the process is done for the whole flame, the width, height and origin of both flames are found, and images that can be easily compared are created.

A Semiautonomous Deep Learning System to Reduce False-Positive Findings in Screening Mammography.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning algorithm was trained using 123,248 2D digital mammograms (6,161 cancers) and a retrospective study was performed on three nonoverlapping datasets of 14,831 screening mammography examinations (1,026 cancers) from 2 US and 1 UK institutions (2008-2017). The standalone performance of humans and AI was compared. Human+AI performance was simulated to examine reductions in the cancer detection rate, number of examinations, false positive callbacks, and benign biopsies. Metrics were adjusted to mimic the natural distribution of a screening population, and bootstrapped confidence intervals (CI) and P values were calculated. Results Retrospective evaluation on all datasets showed minimal changes to the cancer detection rate with use of the AI device (US Dataset 1 P = .02, US Dataset 2 P < .001, UK P < .001, noninferiority margin of 0.25 cancers per 1000 examinations). On US Dataset 1 (11,592 mammograms, 101 cancers, 3810 female patients, mean age 57.3 ± [SD] 10.0 years), the device reduced screening examinations requiring radiologist interpretation by 41.6% [95% CI: 40.6%, 42.4%] (P < .001), diagnostic examinations callbacks by 31.1% [28.7%, 33.4%] (P < .001), and benign needle biopsies by 7.4% [4.1%, 12.4%] (P < .001). US Dataset 2 (1362 mammograms, 330 cancers, 1293 female patients, mean age 55.4 ± 10.5 years) had reductions of 19.5% [16.9%, 22.1%] (P < .001), 11.9% [8.6%, 15.7%] (P< .001), and 6.5% [0.0%, 19.0%] (P = .08), respectively. The UK dataset (1877 mammograms, 595 cancers, 1491 female patients, mean age 63.5 ± 7.1 SD) had reductions of 36.8% [34.4%, 39.7%] (P < .001), 17.1% [5.9%, 30.1%] (P < .001), and 5.9% [2.9%, 11.5%] (P < .001), respectively. Conclusion This work demonstrates the potential of a semiautonomous breast cancer screening system to reduce false positives, unnecessary procedures, patient anxiety, and medical expenses. Published under a CC BY 4.0 license.

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.

Diagnostic Imaging for Retinoblastoma Cancer Staging: Guide for Providing Essential Insights for Ophthalmologists and Oncologists.

Retinoblastoma is the most common cause of all intraocular pediatric malignancies. It is caused by the loss of RB1 tumor suppressor gene function, although some tumors occur due to MYCN oncogene amplification with normal RB1 genes. Nearly half of all retinoblastomas occur due to a hereditary germline RB1 pathogenic variant, most of which manifest with bilateral tumors. This germline RB1 mutation also predisposes to intracranial midline embryonal tumors. Accurate staging of retinoblastoma is crucial in providing optimal vision-, eye-, and life-saving treatment. The AJCC Cancer Staging Manual has undergone significant changes, resulting in a universally accepted system with a multidisciplinary approach for managing retinoblastoma. The authors discuss the role of MRI and other diagnostic imaging techniques in the pretreatment assessment and staging of retinoblastoma. A thorough overview of the prevailing imaging standards and evidence-based perspectives on the benefits and drawbacks of these techniques is provided. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material.

Island Girl, Universal Woman: &lt;i&gt;Peig: A Scéal Féin&lt;/i&gt; (1998) as Narrative of Class, Labour and Femininity

This article reappraises the Irish-language autobiography, Peig, as a unique account of the experience of life and work by a woman of the rural Irish speaking subsistence class. Peig is a seminal Irish language text, both as a depiction of class and gender-based conflict, and as an Irish language work that describes intersectional issues of justice, labour, rights and femininity from childhood to old age. I examine the narrative through the lenses of home, poverty, housing, work, marriage, children, friends, foes and loss. Peig depicts great resilience in the face of structural inequalities and localised injustice. The sustaining nature of intergenerational female friendships plays the central role in Peig’s account of her life. Arguing that the text belongs to the shared histories of women across the world, from China to the Middle East and Africa, I conclude that Peig is one of the most emotionally rich books in modern Irish. The article opens new ground in examining the question of class in Irish language literature, concluding that its universal themes continue to resonate in a contemporary multi-ethnic Ireland. Banner Image: Great Blasket (2019), taken by Maciej Brencz. Source: Wikimedia Commons, licensed under CC BY 2.0.

Editor’s note

This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

Intentional function and frequency of reporting verbs across six disciplines: A cluster analysis

Academic writers can convey their attitudes and opinions, or stance, through carefully chosen reporting verbs, which introduce and cite sources while expressing author perspectives. Since reporting verbs reflect different authorial intentions, they can be categorized into different stance acts. However, there is limited research on how reporting verb stance varies across disciplines. In this study, we analysed the stance of reporting verbs in the background sections of 270 academic articles from six disciplines in the Academic Journal Registers Corpus (AJRC) (Gray, 2011). Two cluster analyses yielded three clusters of reporting verb patterns based on act type, and five clusters based on stance type. The distribution of these clusters varied across disciplines, with applied linguistics and history as well as physics and political science showing similar patterns. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

Investigating the effects of discipline and first-language backgrounds on ITAs’ instructional discourse patterns

Using a multidimensional analysis (MDA), this study aimed to increase the understanding of international teaching assistants’ (ITAs’) instructional language use by analyzing co-occurring linguistic features during their mock-teaching presentations in ITA assessment. With this purpose, the study examined the effects of ITAs’ proficiency levels, disciplines, and first-language backgrounds on their discourse patterns. The data comprised a corpus of 165 prospective ITAs’ mock-teaching presentations in ITA assessment. Based on the two dimensions of the twenty-six linguistic features found prevalent in university classroom talk (Csomay, 2005), an additive MDA was performed. Overall, the mock-teaching presentations of more proficient ITAs involved more contextual, directive, and personalized framing discourse features, with interaction effects for the ITAs’ disciplines and first-language backgrounds. The study discusses the implications for ITA training and assessment and directions for future studies. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

The use of code glosses in MA dissertations in English and in Italian: A pilot study in an EMI context

Within the English Medium of Instruction (EMI) framework adopted by many European universities, students listen to lectures, present, write, and take exams in English. Research on EMI settings mostly focused on lectures, leaving scant attention to student writing and in particular to the writing of dissertations in the Italian context. This paper addresses this gap by investigating lexico-grammatical occurrences of selected metadiscursive features adopted in the final dissertations of Italian masters’ students in business and economics courses. The dissertations are retrieved from the MoReThesisCorpus, a large digital repository of theses and dissertations at the University of Modena e Reggio Emilia. By paying close attention to reformulations and exemplifications, our findings reveal a developmental interconnection between spoken and written metadiscursive features. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .

Semi-supervised Learning for Generalizable Intracranial Hemorrhage Detection and Segmentation.

Purpose To develop and evaluate a semi-supervised learning model for intracranial hemorrhage detection and segmentation on an out-of-distribution head CT evaluation set. Materials and Methods This retrospective study used semi-supervised learning to bootstrap performance. An initial "teacher" deep learning model was trained on 457 pixel-labeled head CT scans collected from one U.S. institution from 2010 to 2017 and used to generate pseudo labels on a separate unlabeled corpus of 25 000 examinations from the Radiological Society of North America and American Society of Neuroradiology. A second "student" model was trained on this combined pixel- and pseudo-labeled dataset. Hyperparameter tuning was performed on a validation set of 93 scans. Testing for both classification (n = 481 examinations) and segmentation (n = 23 examinations, or 529 images) was performed on CQ500, a dataset of 481 scans performed in India, to evaluate out-of-distribution generalizability. The semi-supervised model was compared with a baseline model trained on only labeled data using area under the receiver operating characteristic curve, Dice similarity coefficient, and average precision metrics. Results The semi-supervised model achieved a statistically significant higher examination area under the receiver operating characteristic curve on CQ500 compared with the baseline (0.939 [95% CI: 0.938, 0.940] vs 0.907 [95% CI: 0.906, 0.908]; P = .009). It also achieved a higher Dice similarity coefficient (0.829 [95% CI: 0.825, 0.833] vs 0.809 [95% CI: 0.803, 0.812]; P = .012) and pixel average precision (0.848 [95% CI: 0.843, 0.853]) vs 0.828 [95% CI: 0.817, 0.828]) compared with the baseline. Conclusion The addition of unlabeled data in a semi-supervised learning framework demonstrates stronger generalizability potential for intracranial hemorrhage detection and segmentation compared with a supervised baseline. Keywords: Semi-supervised Learning, Traumatic Brain Injury, CT, Machine Learning Supplemental material is available for this article. Published under a CC BY 4.0 license. See also the commentary by Swimburne in this issue.

Comparative Analysis of the Avirulence Effectors Produced by the Fungal Stem Rust Pathogen of Wheat.

Crops are constantly exposed to pathogenic microbes. Rust fungi are examples of these harmful microorganisms, which have a major economic impact on wheat production. To protect themselves from pathogens like rust fungi, plants employ a multilayered immune system that includes immunoreceptors encoded by resistance genes. Significant efforts have led to the isolation of numerous resistance genes against rust fungi in cereals, especially in wheat. However, the evolution of virulence of rust fungi hinders the durability of resistance genes as a strategy for crop protection. Rust fungi, like other biotrophic pathogens, secrete an arsenal of effectors to facilitate infection, and these are the molecules that plant immunoreceptors target for pathogen recognition and mounting defense responses. When recognized, these effector proteins are referred to as avirulence (Avr) effectors. Despite the many predicted effectors in wheat rust fungi, only five Avr genes have been identified, all from wheat stem rust. Knowledge of the Avr genes and their variation in the fungal population will inform deployment of the most appropriate wheat disease-resistance genes for breeding and farming. The review provides an overview of methodologies as well as the validation techniques that have been used to characterize Avr effectors from wheat stem rust. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Fungal Cell Wall-Associated Effectors: Sensing, Integration, Suppression, and Protection.

The cell wall (CW) of plant-interacting fungi, as the direct interface with host plants, plays a crucial role in fungal development. A number of secreted proteins are directly associated with the fungal CW, either through covalent or non-covalent interactions, and serve a range of important functions. In the context of plant-fungal interactions many are important for fungal development in the host environment and may therefore be considered fungal CW-associated effectors (CWAEs). Key CWAE functions include integrating chemical/physical signals to direct hyphal growth, interfering with plant immunity, and providing protection against plant defenses. In recent years, a diverse range of mechanisms have been reported that underpin their roles, with some CWAEs harboring conserved motifs or functional domains, while others are reported to have novel features. As such, the current understanding regarding fungal CWAEs is systematically presented here from the perspective of their biological functions in plant-fungal interactions. An overview of the fungal CW architecture and the mechanisms by which proteins are secreted, modified, and incorporated into the CW is first presented to provide context for their biological roles. Some CWAE functions are reported across a broad range of pathosystems or symbiotic/mutualistic associations. Prominent are the chitin interacting-effectors that facilitate fungal CW modification, protection, or suppression of host immune responses. However, several alternative functions are now reported and are presented and discussed. CWAEs can play diverse roles, some possibly unique to fungal lineages and others conserved across a broad range of plant-interacting fungi. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Do Growers Using Solo Fungicides Affect the Durability of Disease Control of Growers Using Mixtures and Alternations? The Case of Spot-Form Net Blotch in Western Australia.

Growers often use alternations or mixtures of fungicides to slow down the development of resistance to fungicides. However, within a landscape, some growers will implement such resistance management methods, whereas others do not, and may even apply solo components of the resistance management program. We investigated whether growers using solo components of resistant management programs affect the durability of disease control in fields of those who implement fungicide resistance management. We developed a spatially implicit semidiscrete epidemiological model for the development of fungicide resistance. The model simulates the development of epidemics of spot-form net blotch disease, caused by the pathogen Pyrenophora teres f. maculata. The landscape comprises three types of fields, grouped according to their treatment program, with spore dispersal between fields early in the cropping season. In one field type, a fungicide resistance management method is implemented, whereas in the two others, it is not, with one of these field types using a component of the fungicide resistance management program. The output of the model suggests that the use of component fungicides does affect the durability of disease control for growers using resistance management programs. The magnitude of the effect depends on the characteristics of the pathosystem, the degree of inoculum mixing between fields, and the resistance management program being used. Additionally, although increasing the amount of the solo component in the landscape generally decreases the lifespan within which the resistance management program provides effective control, situations exist where the lifespan may be minimized at intermediate levels of the solo component fungicide. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The WY Domain of an RxLr Effector Drives Interactions with a Host Target Phosphatase to Mimic Host Regulatory Proteins and Promote Phytophthora infestans Infection.

Plant pathogens manipulate the cellular environment of the host to facilitate infection and colonization that often lead to plant diseases. To accomplish this, many specialized pathogens secrete virulence proteins called effectors into the host cell, which subvert processes such as immune signaling, gene transcription, and host metabolism. Phytophthora infestans, the causative agent of potato late blight, employs an expanded repertoire of RxLR effectors with WY domains to manipulate the host through direct interaction with protein targets. However, our understanding of the molecular mechanisms underlying the interactions between WY effectors and their host targets remains limited. In this study, we performed a structural and biophysical characterization of the P. infestans WY effector Pi04314 in complex with the potato Protein Phosphatase 1-c (PP1c). We elucidate how Pi04314 uses a WY domain and a specialized C-terminal loop carrying a KVxF motif that interact with conserved surfaces on PP1c, known to be used by host regulatory proteins for guiding function. Through biophysical and in planta analyses, we demonstrate that Pi04314 WY or KVxF mutants lose their ability to bind PP1c. The loss of PP1c binding correlates with changes in PP1c nucleolar localization and a decrease in lesion size in plant infection assays. This study provides insights into the manipulation of plant hosts by pathogens, revealing how effectors exploit key regulatory interfaces in host proteins to modify their function and facilitate disease. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.