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Although Corsica is the fourth-largest island in the Mediterranean, its archaeology is internationally less well known than that of many smaller Mediterranean islands. La Balagne Landscape Project (LBLP) was initiated to redress this situation but also, for the first time in Corsica, to undertake a surface survey in order systematically to recover and record archaeological features and materials over an extensive area. La Balagne in northwestern Corsica was chosen as the region of study because of its recent history of archaeological excavation and find spot documentation. During the course of three survey field seasons in 2006-2008, elements of seven communes (administrative districts) were surveyed and a total of 89 temporally distinct 'evidence zones' (e-zones) were discovered. The data collected by the LBLP to date suggest significant differences in the locus of settlements over time, but little change to the landscape. The coastal and near coastal zone has been the focus for habitation from the Early Neolithic onwards, but the interior was not extensively exploited until the recent past. [ABSTRACT FROM AUTHOR]

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Philippe Weitz reports a relationship with Stratipath AB that includes: employment. Mattias Rantalainen reports a relationship with Stratipath AB that includes: equity or stocks. Johan Hartman reports a relationship with Stratipath AB that includes: equity or stocks. Kimmo Kartasalo reports a relationship with Clinsight AB that includes: equity or stocks. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Background and Purpose: Magnetic resonance imaging (MRI) is heavily relied upon for the diagnosis and monitoring of multiple sclerosis (MS), a chronic, demyelinating disease of the central nervous system. Serum biomarkers may serve as an accessible tool for increasing sensitivity, improving accessibility, corroborating symptoms, and providing additional data to guide clinical management. This scoping review investigates the current understanding of how the serum biomarker glial fibrillary acidic protein (sGFAP) relates to brain MRI metrics., Methods: We adhered to the Joanna Briggs Institute methodology for scoping reviews and the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. The databases Medline (Ovid), Embase (Ovid), CINAHL (Ebsco), and Web of Science (University of British Columbia institutional access) were searched on August 24, 2023 using a combination of medical subject headings and keyword terms for the topic of serum biomarkers in MS., Results: A total of 9880 articles were retrieved in total of which 6271 unique titles and abstracts were screened. Twelve of the 259 resultant papers contained sGFAP data and proceeded to extraction. It was found that lesion MRI metrics generally had a positive relationship with sGFAP, while gray matter and white matter metrics, including normal-appearing white matter, were related negatively or not at all., Conclusions: These results highlight that while sGFAP may not be specific for MS, it may have utility for increasing sensitivity in postdiagnosis monitoring of MS progression., (© 2024 The Author(s). Journal of Neuroimaging published by Wiley Periodicals LLC on behalf of American Society of Neuroimaging.)

Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background and Objective: The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology., Methods: We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The registration time is low, allowing one to perform efficient registration even for large datasets. The method was proposed for the ACROBAT 2023 challenge organized during the MICCAI 2023 conference and scored 1st place. The method is released as open-source software., Results: The proposed method is evaluated using three open datasets: (i) Automatic Nonrigid Histological Image Registration Dataset (ANHIR), (ii) Automatic Registration of Breast Cancer Tissue Dataset (ACROBAT), and (iii) Hybrid Restained and Consecutive Histological Serial Sections Dataset (HyReCo). The target registration error (TRE) is used as the evaluation metric. We compare the proposed algorithm to other state-of-the-art solutions, showing considerable improvement. Additionally, we perform several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset., Conclusions: The article presents an automatic and robust registration method that outperforms other state-of-the-art solutions. The method does not require any fine-tuning to a particular dataset and can be used out-of-the-box for numerous types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level (resolution up to 220k x 220k). We provide free access to the software. The results are fully and easily reproducible. The proposed method is a significant contribution to improving the WSI registration quality, thus advancing the field of digital pathology., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

This systematic review evaluates the efficacy of self-care interventions for atrial fibrillation (AF), focusing on strategies for maintenance, monitoring, and management applied individually or in combination. Adhering to the 2020 PRISMA guidelines, the search strategy spanned literature from 2005 to 2023, utilizing keywords and subject headings for "atrial fibrillation" and "self-care" combined with the Boolean operator AND. The databases searched included Medline, Embase, and CINAHL. The initial search, conducted on February 17, 2021, and updated on May 16, 2023, identified 5160 articles, from which 2864 unique titles and abstracts were screened. After abstract screening, 163 articles were reviewed in full text, resulting in 27 articles being selected for data extraction; these studies comprised both observational and randomized controlled trial designs. A key finding in our analysis reveals that self-care interventions, whether singular, dual, or integrated across all three components, resulted in significant improvements across patient-reported, clinical, and healthcare utilization outcomes compared to usual care. Educational interventions, often supported by in-person sessions or telephone follow-ups, emerged as a crucial element of effective AF self-care. Additionally, the integration of mobile and web-based technologies alongside personalized education showed promise in enhancing outcomes, although their full potential remains underexplored. This review highlights the importance of incorporating comprehensive, theory-informed self-care interventions into routine clinical practice and underscores the need for ongoing innovation and the implementation of evidence-based strategies. The integration of education and technology in AF self-care aligns with the recommendations of leading health organizations, advocating for patient-centered, technology-enhanced approaches to meet the evolving needs of the AF population., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information., Competing Interests: Declaration of Competing Interest This manuscript is an honest and transparent account of the research being pursued. No important aspects of the study have been omitted. No relationships with other people or organizations that could inappropriately introduce a bias have been established. Neither this manuscript nor any parts of its content are currently under consideration or published elsewhere in any language., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices., Material and Methods: This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology., Results: The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data., Discussion: The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gianmaria Silvello reports financial support was provided by 10.13039/501100000780European Commission. Filippo Fragetta is an author of the paper and a member of the editorial board of JPI., (© 2023 The Authors.)

Computational pathology targets the automatic analysis of Whole Slide Images (WSI). WSIs are high-resolution digitized histopathology images, stained with chemical reagents to highlight specific tissue structures and scanned via whole slide scanners. The application of different parameters during WSI acquisition may lead to stain color heterogeneity, especially considering samples collected from several medical centers. Dealing with stain color heterogeneity often limits the robustness of methods developed to analyze WSIs, in particular Convolutional Neural Networks (CNN), the state-of-the-art algorithm for most computational pathology tasks. Stain color heterogeneity is still an unsolved problem, although several methods have been developed to alleviate it, such as Hue-Saturation-Contrast (HSC) color augmentation and stain augmentation methods. The goal of this paper is to present Data-Driven Color Augmentation (DDCA), a method to improve the efficiency of color augmentation methods by increasing the reliability of the samples used for training computational pathology models. During CNN training, a database including over 2 million H&E color variations collected from private and public datasets is used as a reference to discard augmented data with color distributions that do not correspond to realistic data. DDCA is applied to HSC color augmentation, stain augmentation and H&E-adversarial networks in colon and prostate cancer classification tasks. DDCA is then compared with 11 state-of-the-art baseline methods to handle color heterogeneity, showing that it can substantially improve classification performance on unseen data including heterogeneous color variations., Competing Interests: The authors declare that there are no competing interests., (© 2022 The Authors.)

Exa-scale volumes of medical data have been produced for decades. In most cases, the diagnosis is reported in free text, encoding medical knowledge that is still largely unexploited. In order to allow decoding medical knowledge included in reports, we propose an unsupervised knowledge extraction system combining a rule-based expert system with pre-trained Machine Learning (ML) models, namely the Semantic Knowledge Extractor Tool (SKET). Combining rule-based techniques and pre-trained ML models provides high accuracy results for knowledge extraction. This work demonstrates the viability of unsupervised Natural Language Processing (NLP) techniques to extract critical information from cancer reports, opening opportunities such as data mining for knowledge extraction purposes, precision medicine applications, structured report creation, and multimodal learning. SKET is a practical and unsupervised approach to extracting knowledge from pathology reports, which opens up unprecedented opportunities to exploit textual and multimodal medical information in clinical practice. We also propose SKET eXplained (SKET X), a web-based system providing visual explanations about the algorithmic decisions taken by SKET. SKET X is designed/developed to support pathologists and domain experts in understanding SKET predictions, possibly driving further improvements to the system., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Filippo Fraggetta is an author of this work and a member of the editorial board., (© 2022 The Authors.)

The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3'769 clinical images and reports, provided by two hospitals and tested on over 11'000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations., (© 2022. The Author(s).)

Convolutional neural networks (CNNs) are state-of-the-art computer vision techniques for various tasks, particularly for image classification. However, there are domains where the training of classification models that generalize on several datasets is still an open challenge because of the highly heterogeneous data and the lack of large datasets with local annotations of the regions of interest, such as histopathology image analysis. Histopathology concerns the microscopic analysis of tissue specimens processed in glass slides to identify diseases such as cancer. Digital pathology concerns the acquisition, management and automatic analysis of digitized histopathology images that are large, having in the order of 100 ' 000 2 pixels per image. Digital histopathology images are highly heterogeneous due to the variability of the image acquisition procedures. Creating locally labeled regions (required for the training) is time-consuming and often expensive in the medical field, as physicians usually have to annotate the data. Despite the advances in deep learning, leveraging strongly and weakly annotated datasets to train classification models is still an unsolved problem, mainly when data are very heterogeneous. Large amounts of data are needed to create models that generalize well. This paper presents a novel approach to train CNNs that generalize to heterogeneous datasets originating from various sources and without local annotations. The data analysis pipeline targets Gleason grading on prostate images and includes two models in sequence, following a teacher/student training paradigm. The teacher model (a high-capacity neural network) automatically annotates a set of pseudo-labeled patches used to train the student model (a smaller network). The two models are trained with two different teacher/student approaches: semi-supervised learning and semi-weekly supervised learning. For each of the two approaches, three student training variants are presented. The baseline is provided by training the student model only with the strongly annotated data. Classification performance is evaluated on the student model at the patch level (using the local annotations of the Tissue Micro-Arrays Zurich dataset) and at the global level (using the TCGA-PRAD, The Cancer Genome Atlas-PRostate ADenocarcinoma, whole slide image Gleason score). The teacher/student paradigm allows the models to better generalize on both datasets, despite the inter-dataset heterogeneity and the small number of local annotations used. The classification performance is improved both at the patch-level (up to κ=0.6127±0.0133 from κ=0.5667±0.0285), at the TMA core-level (Gleason score) (up to κ=0.7645±0.0231 from κ=0.7186±0.0306) and at the WSI-level (Gleason score) (up to κ=0.4529±0.0512 from κ=0.2293±0.1350). The results show that with the teacher/student paradigm, it is possible to train models that generalize on datasets from entirely different sources, despite the inter-dataset heterogeneity and the lack of large datasets with local annotations., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate the scarcity of annotated data include: using transfer learning, data augmentation and training the models with less expensive image-level annotations (weakly-supervised learning). However, it is not clear how to combine the use of transfer learning in a CNN model when different data sources are available for training or how to leverage from the combination of large amounts of weakly annotated images with a set of local region annotations. This paper aims to evaluate CNN training strategies based on transfer learning to leverage the combination of weak and strong annotations in heterogeneous data sources. The trade-off between classification performance and annotation effort is explored by evaluating a CNN that learns from strong labels (region annotations) and is later fine-tuned on a dataset with less expensive weak (image-level) labels., Results: As expected, the model performance on strongly annotated data steadily increases as the percentage of strong annotations that are used increases, reaching a performance comparable to pathologists ([Formula: see text]). Nevertheless, the performance sharply decreases when applied for the WSI classification scenario with [Formula: see text]. Moreover, it only provides a lower performance regardless of the number of annotations used. The model performance increases when fine-tuning the model for the task of Gleason scoring with the weak WSI labels [Formula: see text]., Conclusion: Combining weak and strong supervision improves strong supervision in classification of Gleason patterns using tissue microarrays (TMA) and WSI regions. Our results contribute very good strategies for training CNN models combining few annotated data and heterogeneous data sources. The performance increases in the controlled TMA scenario with the number of annotations used to train the model. Nevertheless, the performance is hindered when the trained TMA model is applied directly to the more challenging WSI classification problem. This demonstrates that a good pre-trained model for prostate cancer TMA image classification may lead to the best downstream model if fine-tuned on the WSI target dataset. We have made available the source code repository for reproducing the experiments in the paper: https://github.com/ilmaro8/Digital_Pathology_Transfer_Learning.

The objective of this study was to compare the gene expression levels of the agr locus and prfA gene during adhesion and biofilm formation by four L. monocytogenes isolates (2 biofilm-forming and 2 non-forming) on stainless steel and polystyrene surfaces at different temperatures (10 °C, 20 °C and 37 °C), and times (8 h, 12 h, 24 h and 48 h). The agrA and prfA genes were expressed at higher levels than the agrBCD genes. The levels of agr locus expression were higher in the biofilm-forming strains, and the greatest difference between biofilm-forming and non-forming isolates was observed for the agrB, agrC and agrD genes. However, no difference in the expression of the prfA gene was seen among the isolates, independent of the biofilm-forming ability. Maximum expression of the agr locus and prfA gene was observed at 37 °C, whereas expression was lowest at 10 °C. The agr locus, and particularly the agrB, agrC and agrD genes, is important in the initial adhesion phase of biofilm production by L. monocytogenes, with this expression independent of prfA. In addition, the agr locus and prfA gene expression levels were strongly influenced by time and temperature., (Copyright © 2019. Published by Elsevier B.V.)

Reverse Transcription quantitative PCR (RT-qPCR) is a technique for gene expression profiling with high sensibility and reproducibility. However, to obtain accurate results, it depends on data normalization by using endogenous reference genes whose expression is constitutive or invariable. Although the technique is widely used in plant stress analyzes, the stability of reference genes for iron toxicity in rice (Oryza sativa L.) has not been thoroughly investigated. Here, we tested a set of candidate reference genes for use in rice under this stressful condition. The test was performed using four distinct methods: NormFinder, BestKeeper, geNorm and the comparative ΔCt. To achieve reproducible and reliable results, Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines were followed. Valid reference genes were found for shoot (P2, OsGAPDH and OsNABP), root (OsEF-1a, P8 and OsGAPDH) and root+shoot (OsNABP, OsGAPDH and P8) enabling us to perform further reliable studies for iron toxicity in both indica and japonica subspecies. The importance of the study of other than the traditional endogenous genes for use as normalizers is also shown here.

Iron (Fe) is an essential microelement for all living organisms playing important roles in several metabolic reactions. Rice (Oryza sativa L.) is commonly cultivated in paddy fields, where Fe goes through a reduction reaction from Fe 3+ to Fe 2+ . Since Fe 2+ is more soluble, it can reach toxic levels inside plant cells, constituting an important target for studies. Here we aimed to verify morphological changes of different rice genotypes focusing on deciphering the underlying molecular network induced upon Fe excess treatments with special emphasis on the role of four WRKY transcription factors. The transcriptional response peak of these WRKY transcription factors in rice seedlings occurs at 4 days of exposition to iron excess. OsWRKY55-like, OsWRKY46, OsWRKY64, and OsWRKY113 are up-regulated in BR IRGA 409, an iron-sensitive genotype, while in cultivars Nipponbare (moderately resistant) and EPAGRI 108 (resistant) the expression profiles of these transcription factors show similar behaviors. Here is also shown that some cis-regulatory elements known to be involved in other different stress responses can be linked to conditions of iron excess. Overall, here we support the role of WRKY transcription factors in iron stress tolerance with other important steps toward finding why some rice genotypes are more tolerant than others.

The four NGATHA genes (NGA) form a small subfamily within the large family of B3-domain transcription factors of Arabidopsis thaliana. NGA genes act redundantly to direct the development of the apical tissues of the gynoecium, the style, and the stigma. Previous studies indicate that NGA genes could exert this function at least partially by directing the synthesis of auxin at the distal end of the developing gynoecium through the upregulation of two different YUCCA genes, which encode flavin monooxygenases involved in auxin biosynthesis. We have compared three developing pistil transcriptome data sets from wildtype, nga quadruple mutants, and a 35S::NGA3 line. The differentially expressed genes showed a significant enrichment for auxin-related genes, supporting the idea of NGA genes as major regulators of auxin accumulation and distribution within the developing gynoecium. We have introduced reporter lines for several of these differentially expressed genes involved in synthesis, transport and response to auxin in NGA gain- and loss-of-function backgrounds. We present here a detailed map of the response of these reporters to NGA misregulation that could help to clarify the role of NGA in auxin-mediated gynoecium morphogenesis. Our data point to a very reduced auxin synthesis in the developing apical gynoecium of nga mutants, likely responsible for the lack of DR5rev::GFP reporter activity observed in these mutants. In addition, NGA altered activity affects the expression of protein kinases that regulate the cellular localization of auxin efflux regulators, and thus likely impact auxin transport. Finally, protein accumulation in pistils of several ARFs was differentially affected by nga mutations or NGA overexpression, suggesting that these accumulation patterns depend not only on auxin distribution but could be also regulated by transcriptional networks involving NGA factors.