Early Stage Research Articles

The Unfinished Reconstructed Nature of the Last Universal Common Ancestor.

The ultimate consequence of Darwin's theory of common descent implies that all life on earth descends ultimately from a common ancestor. Biochemistry and molecular biology now provide sufficient evidence of shared ancestry of all extant life forms. However, the nature of the Last Universal Common Ancestor (LUCA) has been a topic of much debate over the years. This review offers a historical perspective on different attempts to infer LUCA's nature, exploring the debate surrounding its complexity. We further examine how different methodologies identify sets of ancient protein that exhibit only partial overlap. For example, different bioinformatic approaches have identified distinct protein subunits from the ATP synthetase identified as potentially inherited from LUCA. Additionally, we discuss how detailed molecular evolutionary analysis of reverse gyrase has modified previous inferences about an hyperthermophilic LUCA based mainly on automatic bioinformatic pipelines. We conclude by emphasizing the importance of developing a database dedicated to studying genes and proteins traceable back to LUCA and earlier stages of cellular evolution. Such a database would house the most ancient genes on earth.

Development of neuronal timescales in human cortical organoids and rat hippocampus dissociated cultures.

To support complex cognition, neuronal circuits must integrate information across multiple temporal scales, ranging from milliseconds to decades. Neuronal timescales describe the duration over which activity within a network persists, posing a putative explanatory mechanism for how information might be integrated over multiple temporal scales. Little is known about how timescales develop in human neural circuits or other model systems, limiting insight into how the functional dynamics necessary for cognition emerge. In our work, we show that neuronal timescales develop in a non-linear fashion in both human cortical organoids and dissociated rat hippocampus cultures. We use spectral parameterization of spiking activity to extract an estimate of neuronal timescale that is unbiased by co-evolving oscillations. Cortical organoid timescales begin to increase around month 6 post-differentiation. We complement these findings with an analysis of timescales in rodent hippocampal dissociated cultures over development and see that timescales decrease from in vitro days 13-23 before stabilizing. We speculate that cortical organoid development over the duration studied here reflects an earlier stage of a generalized developmental timeline in contrast to the rodent hippocampal cultures, potentially accounting for differences in timescale developmental trajectories. The fluctuation of timescales might be an important developmental feature that reflects the changing complexity and information capacity in developing neuronal circuits.

The possible benefit of artificial intelligence in an organized population-related screening program : Initial results and perspective

Mammography screening programs (MSP) have shown that breast cancer can be detected at an earlier stage enabling less invasive treatment and leading to abetter survival rate. The considerable numbers of interval breast cancer (IBC) and the additional examinations required, the majority of which turn out not to be cancer, are critically assessed. In recent years companies and universities have used machine learning (ML) to develop powerful algorithms that demonstrate astonishing abilities to read mammograms. Can such algorithms be used to improve the quality of MSP? The original screening mammographies of 251 cases with IBC were retrospectively analyzed using the software ProFound AI® (iCAD) and the results were compared (case score, risk score) with acontrol group. The relevant current literature was also studied. The distributions of the case scores and the risk scores were markedly shifted to higher risks compared to the control group, comparable to the results of other studies. Retrospective studies as well as our own data show that artificial intelligence (AI) could change our approach to MSP in the future in the direction of personalized screening and could enable asignificant reduction in the workload of radiologists, fewer additional examinations and areduced number of IBCs; however, the results of prospective studies are needed before implementation.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Meaning-making and transformative engagement – notes on Gunther Kress’s social semiotic and multimodal approach to learning

Abstract Against the background of a longstanding collaboration between Gunther Kress’s research group in London and my own research group in Stockholm, I reflect, in this paper, on the role of Kress’s ideas in our joint development of a social semiotic, multimodal, and design-oriented approach to learning, an approach which sees learning as performative, and as an activity in which learners create their own learning paths. I first discuss how, for Kress, this path has three elements, the affordances of the learning resources available to the learner, the learner’s ‘interest’ which turns aspects of these resources into ‘prompts’ for learning, and the learner’s active interpretation and transformation of these aspects, the results of which can then be recognized and valued as ‘signs of learning’. However, recognizing learning also needs to take account of the dimension of time, so as to make it possible to assess whether learners have gained knowledges and skills they did not have at an earlier stage. I then discuss the role of context in Kress’s thinking about learning. For Kress, context is another vital aspect of a social semiotic theory of learning. On the one hand, Kress focuses here on the specific, unique contexts in which individual learners create their own learning paths. On the other hand, he recognizes that signs will always carry social and political relations. Reflecting on the dynamic relation between individual learners and the way institutions regulate ways of learning, I discuss both the continued role of institutional learning contexts and their hidden curricula, and the way emerging technologies facilitate individual learning paths and interactive, participatory forms of learning.

Lateral Thinking: Pathway Specific Neurodegeneration of the Cortical Cholinergic System in Alzheimer's Disease.

Atrophy of the nucleus basalis of Meynert (NBM) is an early indicator of Alzheimer's disease (AD). However, reduced integrity of the NBM white matter tracts may be more relevant for cognitive impairment and progression to dementia than NBM volume. Research is needed to compare differences in NBM volume and integrity of the lateral and medial NBM tracts across early and later stages of AD progression. 187 participants were included in this study who were either healthy controls (HC; n=50) or had early mild cognitive impairment (EMCI; n=50), late MCI (LMCI; n=37), or AD (n=50). NBM volume was calculated using voxel-based morphometry and mean diffusivity (MD) of the lateral and medial NBM tracts were extracted using probabilistic tractography. Between group differences in NBM volume and tract MD were compared using linear mixed models controlling for age, sex, and either total intracranial volume or MD of a control mask, respectively. Associations between NBM volume and tract MD with executive function, memory, language, and visuospatial function were also analysed. NBM volume was smallest in AD followed by LMCI (p<0.0001), with no difference between EMCI and HC. AD had highest MD for both tracts compared to all other groups (p<0.001). Both MCI groups had higher lateral tract MD compared to HC (p<0.05). Medial tract MD was higher in LMCI (p=0.008), but not EMCI (p=0.09) compared to HC. Higher lateral tract MD was associated with executive function (p=0.001) and language (p=0.02). Integrity of the lateral NBM tract is most sensitive to the earliest stages of AD and should be considered an important therapeutic target for early detection and intervention.

Resilient cloud cluster with DevSecOps security model, automates a data analysis, vulnerability search and risk calculation

Automated, secure software development is an important digitalization task, solved with the DevSecOps approach. An important part of the DevSecOps approach is continuous risk assessment, which is necessary to identify and evaluate risk factors. Combining the development cycle with continuous risk assessment creates software development and operation synergies and minimizes vulnerabilities. The article presents the main methods of deploying web applications and ways to increase information security at all stages of product development. It also compares different types of infrastructures and cloud computing providers and analyzes modern tools used to automate processes. The cloud cluster was deployed using Terraform and the Jenkins pipeline, written in the Groovy programming language, which checks program code for vulnerabilities and allows you to fix violations at the earliest stages of developing secure web applications. The developed cluster implements the proposed algorithm for automated risk assessment based on the calculation (modeling) of cloud infrastructure threats and vulnerabilities, which operates in real-time, periodically collecting all information and adjusting the system according to the risk and applied controls. The algorithm for calculating risk and losses is based on statistical data and FAIR information risk assessment methodology. The risk value obtained using the proposed method is quantitative, which allows more efficient forecasting of information security costs in software development.

Planning sentences and sentence intonation in Estonian

The notion of advance planning of sentence intonation is grounded in the positive correlation between the sentence-initial intonation peaks and sentence duration. This study examined real-time sentence planning and intonation using visual world speech production. In two eye-tracking experiments, native Estonian speakers described transitive events involving multiple actors. Conceptual complexity of the resulting picture descriptions was manipulated through a pictorial design, while sentence length was controlled for by manipulating specific task characteristics. In Experiment I, conceptual complexity of the picture descriptions varied together with linguistic complexity, while linguistic complexity was held constant in Experiment II. As the conceptual complexity of utterances increased, the duration of naming gazes also increased, indicating less incremental conceptual planning. Notably, while utterance-initial intonation peaks did not correlate with the relative duration of naming gazes, they were influenced by utterance length. These findings highlight advance planning of intonation in Estonian. Furthermore, they suggest that intonation planning depends on linguistic information that is rapidly activated after establishing a comprehensive conceptual framework during earliest stages of preverbal planning.

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth, and interactive QC with afni_proc.py and more.

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single-subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, afni_proc.py. These items include a modular HTML document that covers full single-subject processing from the raw data through statistical modeling, several review scripts in the results directory of processed data, and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria, or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each "QC block," as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.

Expression analysis of thg1l during Xenopus laevis development.

The tRNA-histidine guanylyltransferase 1-like (THG1L), also known as induced in high glucose-1 (IHG-1), encodes for an essential mitochondria-associated protein highly conserved throughout evolution, that catalyses the 3'-5' addition of a guanine to the 5'-end of tRNA-histidine (tRNAHis). Previous data indicated that THG1L plays a crucial role in the regulation of mitochondrial biogenesis and dynamics, in ATP production, and is critically involved in the modulation of apoptosis, cell-cycle progression and survival, as well as in cellular stress responses and redox homeostasis. Dysregulations of THG1L expression play a central role in various pathologies, including nephropathies, and neurodevelopmental disorders often characterized by developmental delay and cerebellar ataxia. Despite the essential role of THG1L, little is known about its expression during vertebrate development. Herein, we examined the detailed spatio-temporal expression of this gene in the developing Xenopus laevis. Our results show that thg1l is maternally inherited and its temporal expression suggests a role during the earliest stages of embryogenesis. Spatially, thg1l mRNA localizes in the ectoderm and marginal zone mesoderm during early stages of development. Then, at tadpole stages, thg1l transcripts mostly localise in neural crests and their derivatives, somites, developing kidney and central nervous system, therefore largely coinciding with territories displaying intense energy metabolism during organogenesis in Xenopus.

Blood Flow Restriction Exercise as a Novel Conservative Standard in Patients with Knee Osteoarthritis—A Narrative Review

Knee osteoarthritis is a prevalent joint disease affecting millions of individuals globally. While total knee arthroplasty is an effective treatment for advanced stages of KOA, it may not be suitable for earlier stages or younger patients. Supervised exercise therapy has proven to be the first-line treatment of preference in tackling pain and disability caused by KOA. However, the high intensities required to induce positive muscle adaptations are not indicated in this population, as this is often accompanied by pain, discomfort, and frustration, leaving low-load resistance training as the only feasible method of treatment. Recently, the use of blood flow restriction training has begun to emerge as a substitute for high-load resistance training. With BFRT, a cuff is applied around the proximal aspect of the affected limb, causing partial arterial and full venous occlusion, thereby inducing localized hypoxia and the accumulation of metabolites, mimicking the effects of high-load resistance training, albeit with low loads. Consequently, BFRT might offer a suitable and more effective alternative for KOA patients who are not (yet) eligible for TKA compared to traditional exercise therapy. This review aims to summarize the current evidence as regards the application of Blood Flow Restriction in exercise therapy for knee osteoarthritis patients, with particular consideration of the underlying mechanisms and its safety, as well as general guidelines for practical implementation in clinical practice. In doing so, this narrative review aims to create a framework for translating from theory into practice.

Statistical methods for discrimination of STR genotypes using high resolution melt curve data.

Despite the improvements in forensic DNA quantification methods that allow for the early detection of low template/challenged DNA samples, complicating stochastic effects are not revealed until the final stage of the DNA analysis workflow. An assay that would provide genotyping information at the earlier stage of quantification would allow examiners to make critical adjustments prior to STR amplification allowing for potentially exclusionary information to be immediately reported. Specifically, qPCR instruments often have dissociation curve and/or high-resolution melt curve (HRM) capabilities; this, coupled with statistical prediction analysis, could provide additional information regarding STR genotypes present. Thus, this study aimed to evaluate Qiagen's principal component analysis (PCA)-based ScreenClust® HRM® software and a linear discriminant analysis (LDA)-based technique for their abilities to accurately predict genotypes and similar groups of genotypes from HRM data. Melt curves from single source samples were generated from STR D5S818 and D18S51 amplicons using a Rotor-Gene® Q qPCR instrument and EvaGreen® intercalating dye. When used to predict D5S818 genotypes for unknown samples, LDA analysis outperformed the PCA-based method whether predictions were for individual genotypes (58.92% accuracy) or for geno-groups (81.00% accuracy). However, when a locus with increased heterogeneity was tested (D18S51), PCA-based prediction accuracy rates improved to rates similar to those obtained using LDA (45.10% and 63.46%, respectively). This study provides foundational data documenting the performance of prediction modeling for STR genotyping based on qPCR-HRM data. In order to expand the forensic applicability of this HRM assay, the method could be tested with a more commonly utilized qPCR platform.

Association between neural prosody discrimination and language abilities in toddlers: a functional near-infrared spectroscopy study

BackgroundLanguage delay affects near- and long-term social communication and learning in toddlers, and, an increasing number of experts pay attention to it. The development of prosody discrimination is one of the earliest stages of language development in which key skills for later stages are mastered. Therefore, analyzing the relationship between brain discrimination of speech prosody and language abilities may provide an objective basis for the diagnosis and intervention of language delay.MethodsIn this study, all cases(n = 241) were enrolled from a tertiary women’s hospital, from 2021 to 2022. We used functional near-infrared spectroscopy (fNIRS) to assess children’s neural prosody discrimination abilities, and a Chinese communicative development inventory (CCDI) were used to evaluate their language abilities.ResultsNinety-eight full-term and 108 preterm toddlers were included in the final analysis in phase I and II studies, respectively. The total CCDI screening abnormality rate was 9.2% for full-term and 34.3% for preterm toddlers. Full-term toddlers showed prosody discrimination ability in all channels except channel 5, while preterm toddlers showed prosody discrimination ability in channel 6 only. Multifactorial logistic regression analyses showed that prosody discrimination of the right angular gyrus (channel 3) had a statistically significant effect on language delay (odd ratio = 0.301, P < 0.05) in full-term toddlers. Random forest (RF) regression model presented that prosody discrimination reflected by channels and brain regions based on fNIRS data was an important parameter for predicting language delay in preterm toddlers, among which the prosody discrimination reflected by the right angular gyrus (channel 4) was the most important parameter. The area under the model Receiver operating characteristic (ROC) curve was 0.687.ConclusionsNeural prosody discrimination ability is positively associated with language development, assessment of brain prosody discrimination abilities through fNIRS could be used as an objective indicator for early identification of children with language delay in the future clinical application.

Molecular GBM versus Histopathological GBM: Radiology-Pathology-Genetic Correlation and the New WHO 2021 Definition of Glioblastoma.

Given the recent advances in molecular pathogenesis of tumors, with better correlation with tumor behavior and prognosis, major changes were made to the new 2021 World Health Organization (WHO) classification of CNS tumors, including updated criteria for diagnosis of glioblastoma (GBM). Diagnosis of GBM now requires absence of isocitrate dehydrogenase and histone 3 mutations (IDH-wild-type and H3-wild-type) as the basic cornerstone, with elimination of the IDH-mutant category. The requirements for diagnosis were conventionally histopathological, based on the presence of pathognomonic features such as microvascular proliferation and necrosis. However, even if these histologic features are absent, many lower-grade (WHO grade 2/3) diffuse astrocytic gliomas behave clinically similar to GBM (grade 4). The 2021 WHO classification introduced new molecular criteria that can be used to upgrade the diagnosis of such histologically lower-grade, IDH-wild-type, astrocytomas to GBM. The 3 molecular criteria include: concurrent gain of whole chromosome 7 and loss of whole chromosome 10 (+7/-10); telomerase reverse transcriptase promoter mutation; and epidermal growth factor receptor amplification. Given these changes, it is now strongly recommended to have molecular analysis of WHO grade 2/3 diffuse astrocytic, IDH-wild-type, gliomas in adult patients, as identification of any of the above mutations allows for upgrading the tumor to WHO grade 4 ("molecular GBM") with important prognostic implications. Despite an early stage, there is active ongoing research on the unique MR imaging features of molecular GBM. This paper highlights the differences between "molecular" and "histopathological" GBM, with the aim of providing a basic understanding about these changes.

Allergen-specific B cell responses in oral immunotherapy-induced desensitization, remission, and natural outgrowth in cow's milk allergy.

Antigen-specific memory B cells play a key role in the induction of desensitization and remission to food allergens in oral immunotherapy and in the development of natural tolerance (NT). Here, we characterized milk allergen Bos d 9-specific B cells in oral allergen-specific immunotherapy (OIT) and in children spontaneously outgrowing cow's milk allergy (CMA) due to NT. Samples from children with CMA who received oral OIT (before, during, and after), children who naturally outgrew CMA (NT), and healthy individuals were received from Stanford biobank. Bos d 9-specific B cells were isolated by flow cytometry and RNA-sequencing was performed. Protein profile of Bos d 9-specific B cells was analyzed by proximity extension assay. Increased frequencies of circulating milk allergen Bos d 9-specific B cells were observed after OIT and NT. Milk-desensitized subjects showed the partial acquisition of phenotypic features of remission, suggesting that desensitization is an earlier stage of remission. Within these most significantly expressed genes, IL10RA and TGFB3 were highly expressed in desensitized OIT patients. In both the remission and desensitized groups, B cell activation-, Breg cells-, BCR-signaling-, and differentiation-related genes were upregulated. In NT, pathways associated with innate immunity characteristics, development of marginal zone B cells, and a more established suppressor function of B cells prevail that may play a role in long-term tolerance. The analyses of immunoglobulin heavy chain genes in specific B cells demonstrated that IgG2 in desensitization, IgG1, IgA1, IgA2, IgG4, and IgD in remission, and IgD in NT were predominating. Secreted proteins from allergen-specific B cells revealed higher levels of regulatory cytokines, IL-10, and TGF-β after OIT and NT. Allergen-specific B cells are essential elements in regulating food allergy towards remission in OIT-received and naturally resolved individuals.

Word Type and Frequency Effects on Lexical Decisions Are Process-dependent and Start Early.

When encountering letter strings, we rapidly determine whether they are words. The speed of such lexical decisions (LDs) is affected by word frequency. Apart from influencing late, decision-related, processing stages, frequency has also been shown to affect very early stages, and even the processing of nonwords. We developed a detailed account of the different frequency effects involved in LDs by (1) dividing LDs into processing stages using a combination of Hidden semi-Markov models and multivariate pattern analysis applied to EEG data and (2) using generalized additive mixed models to investigate how the effect of continuous word and nonword frequency differs between these stages. We discovered six stages shared between word types, with the fifth stage consisting of two substages for pseudowords only. In the earliest stages, visual processing was completed faster for frequent words, but took longer for word-like nonwords. Later stages involved an orthographic familiarity assessment followed by an elaborate decision process, both affected differently by frequency. We therefore conclude that frequency indeed affects all processes involved in LDs and that the magnitude and direction of these effects differ both by process and word type.

Predictive risk modeling for child maltreatment detection and enhanced decision-making: Evidence from Danish administrative data.

Child maltreatment is a widespread problem with significant costs for both victims and society. In this retrospective cohort study, we develop predictive risk models using Danish administrative data to predict removal decisions among referred children and assess the effectiveness of caseworkers in identifying children at risk of maltreatment. The study analyzes 195,639 referrals involving 102,309 children Danish Child Protection Services received from April 2016 to December 2017. We implement four machine learning models of increasing complexity, incorporating extensive background information on each child and their family. Our best-performing model exhibits robust predictive power, with an AUC-ROC score exceeding 87%, indicating its ability to consistently rank referred children based on their likelihood of being removed. Additionally, we find strong positive correlations between the model's predictions and various adverse child outcomes, such as crime, physical and mental health issues, and school absenteeism. Furthermore, we demonstrate that predictive risk models can enhance caseworkers' decision-making processes by reducing classification errors and identifying at-risk children at an earlier stage, enabling timely interventions and potentially improving outcomes for vulnerable children.

Evidence-based Prostate Cancer Screening Interventions for Black Men: A Systematic Review.

The databases PubMed, Ovid MEDLINE, CINAHL Plus, and PsychInfo were utilized to examine peer-reviewed publications between 2017 and 2023. Data extracted included implementation plans, outcome measures, intervention details, and results of the study. The Critical Appraisal Skills Programme was used to assess the quality of the evidence presented. Of the 206 full-text articles assessed, three were included in this review. Educational interventions about prostate cancer knowledge with shared and informed decision-making (IDM) features, as well as counseling, treatment options, and healthcare navigation information, may increase prostate cancer screening participation among Black men. Additionally, health partner educational interventions may not improve IDM related to screening participation. The quality of the evidence presented in each article was valid and potentially impactful to the community. Black men face various social determinants of health barriers related to racism, discrimination, cost of health services, time away from work, and lack of trust in the healthcare system when making health-related decisions, including prostate cancer screening participation. A multifactorial intervention approach is required to address these inequities faced by Black men especially as prostate cancer is curable when diagnosed at an earlier stage.

Leukemia Cancer Cells Segmentation and Classification using Machine Learning

Determining the aim of the project is to detect the leukemia at earlier stage with the help of image processing techniques. Leukemia means blood cancer which is featured by the uncontrolled and abnormal production of white blood cells (leukocytes) by the bone marrow in the blood. Acute Lymphoblastic Leukemia (ALL) is a type of leukemia which is more common in children. The term Acute‟ means that leukemia can progress quickly and if not treated may lead to fatal death within few months. Due to its non specific nature of the symptoms and signs of ALL leads wrong diagnosis. Even hematologist finds it difficult to classify the leukemia cells, there manual classification of blood cells is not only time consuming but also inaccurate. Therefore, early identification of leukemia yields in providing the appropriate treatment to the patient. Detection through images is fast and cheap method as there is no special need of equipment for lab testing. We have focused on the changes in the geometry of cells like area, perimeter and statistical parameters like mean and standard deviation which separates white blood cells from other blood components using processing tools like MATLAB. After recognizing its statistical properties, types of leukemia will be identified based on the irregularities in the shape.

Hydration of mafic crustal rocks at high temperature during brittle‐viscous deformation along a strike‐slip plate boundary

AbstractThe Poroshiri ophiolite (Hidaka metamorphic belt, Japan) occurs within a crustal‐scale network of high‐temperature, dextral shear zones that accommodated hundreds of kilometres of displacement due to the opening of the Japan Sea in the Neogene. The opholitic rocks comprise ultramafic, mafic and sedimentary protoliths that have been variably hydrated and metamorphosed. This work investigates the mechanisms and timing of fluid influx relative to viscous deformation in metagabbros and amphibolites deformed during exhumation from granulite‐ to amphibolite‐facies conditions. We consider a range of microstructures, from low strain domains and 1–2 mm thick shear bands to mylonites with a thickness of a few meters. Low strain domains of metagabbros exhibit corona textures with symplectites consisting of pargasitic amphibole (Ed0.7) ‐ anorthitic plagioclase (An80–92) ± orthopyroxene ± clinopyroxene forming around olivine and igneous pyroxene and with similar plagioclase–amphibole‐orthopyroxene ± clinopyroxene granoblastic aggregates in micrometric‐thick fractures. These textures result from hydration under low fluid‐rock ratio, with elevated H2O content only occurring locally (H2O &gt; 1–1.2 wt%). Igneous mineral replacement leads to grain size reduction from 1 mm to ~10 μm. Amphibole exhibits a strong core‐rim zonation primarily controlled by the high diffusivity of Fe, Mg and OH and the low diffusivity of Al. Mineral compositional equilibrium is achieved at the scale of 100–200 μm. In mm‐thick localized shear bands and in metric‐scale mylonitic amphibolites, the heterogeneous mineral composition of amphibole (Ed0.2–0.5) and plagioclase (An40−An80) indicates only partial re‐equilibration (at the scale of 200–500 μm) despite higher fluid‐rock ratios and more pervasive fluid percolation than in metagabbros. Plagioclase–amphibole thermometry and equilibrium phase diagrams indicate that the initial fluid infiltration and corona formation occurred at 800–850°C by fracturing and percolation along grain boundaries. This was followed by the main fluid percolation and mylonitization event, which occurred during exhumation and cooling at conditions of 720–580°C, ~4 kbar. Continuous hydration during retrogression was achieved by the influx of dominantly seawater‐derived fluid, as attested by the high chlorine (Cl) contents (&gt;300–400 ppm) of amphiboles in fractures. The heterogeneous distribution of fractures controls the distribution of fluid from the earliest stages of hydration, creating positive feedback where the growth of hydrous minerals as amphibole (up to +300 vol% of amphibole in high strain areas relative to the low‐strain ones) and the formation of fine‐grained mixed domains that led to further localization of viscous strain and mass transfer (variations of ± 30–40% in major elements). The Poroshiri ophiolite therefore represents a good fossil example of a former transpressional plate boundary where coupled metamorphic and deformation processes were triggered by seawater‐derived fluid that percolated to depths of ~15 km.