Life

Advances in NASAs astrobiology program have demonstrated the feasibility of cultivating plants in space and in analog extraterrestrial habitats. In addition to abiotic stressors, plants grown in terrestrial and space-like environments are challenged by both phytopathogens and opportunistic human pathogens, with implications for plant productivity and human health. The persistence of human-associated pathogens in spacecraft and space stations raises significant concerns regarding food safety. The molecular, biochemical, and signaling mechanisms governing stomatal development and function under microgravity remain poorly understood. We employed an experimental system incorporating human pathogen Salmonella enterica and lettuce microgreens exposed to simulated microgravity through two-dimensional clinorotation to investigate plant innate immunity and stomatal development and function. We further evaluated four lettuce cultivars to determine whether genetic variation impacts these factors under simulated microgravity conditions. Our findings indicate that simulated microgravity significantly influences stomatal development and function, as evidenced by an increase in stomatal density and variable changes to stomatal aperture. Notably, cultivar-dependent variation in stomatal traits and responses to Salmonella enterica was observed under microgravity conditions. Although increased stomatal density was hypothesized to enhance pathogen ingression, internalization was more strongly predicted by cultivar selection and simulated microgravity; simulated microgravity increased ingression, with red pigmented cultivars having less pathogen than green cultivars. These results suggest that targeted selection of cultivars with favorable physiological traits may improve food safety and the viability of crop production systems in space environments. They also suggest that development and function of stomata may change in spaceflight conditions.

Introduction Recent respiratory illness, especially influenza, may trigger acute cardiac events via elevated inflammatory mediators. During the 2018 influenza season in Bangladesh, this study examined whether recent acute clinical respiratory illness (CRI) or laboratory-confirmed influenza was associated with elevated hs-CRP and IL-6, linked to acute cardiac events. Methods A total of 139 participants aged [&ge;]40 were recruited from a Dhaka cardiac hospital: 70 with acute myocardial infarction (AMI), 30 with other acute cardiac events, and 39 healthy individuals. CRI was defined as fever with cough and/or respiratory symptoms within seven days. Respiratory swabs were tested for influenza, and blood was analyzed for hs-CRP and IL-6. Results Median hs-CRP and IL-6 were higher in participants with CRI or influenza but not significantly. Cardiac patients had elevated hs-CRP (9.98 mg/L in other cardiac; 4.86 mg/L in AMI vs. 1.73 mg/L in healthy) and IL-6 (0.1 pg/mL in other cardiac; 0.145 pg/mL in AMI vs. 0.08 pg/mL in healthy) (p<0.001). CRI was not significantly associated with elevated hs-CRP or IL-6, though influenza in healthy participants was linked to higher IL-6. Cardiac patients had a higher risk of hs-CRP [&ge;]3 mg/L and elevated IL-6. Conclusion Cardiac patients showed significantly increased inflammatory markers, but CRI was not clearly linked to inflammation. Further research should assess biomarker utility for early cardiac risk.

Cancer is a major global health issue with rising incidence and mortality. Early detection, tumor characterization, and disease surveillance are crucial for timely and effective treatment, ultimately reducing mortality rates. Liquid biopsy (LB) has emerged as a valuable detection tool offering a non-invasive method to determine tumor-derived biomarkers in body fluids with demonstrated translational potential. To increase biomarker sensitivity, high-throughput sequencing platforms deliver massive volumes of data. Artificial Intelligence (AI) is pivotal in enabling huge and complex data integration. This contribution aims to assess the current state of integrative AI-based research in the LB field and provide methodological guidance. First, we conducted a PubMed search and found that the literature is sparse in studies integrating LB features, particularly by applying AI. When adopting the latter approach, defining the study objectives is crucial to guide the subsequent methodological aspects, including study design, patient selection criteria, sample size, nature of the LB features, and metadata to collect. Specifically, we propose strategies and tools for data preprocessing, including normalization and batch correction, as well as handling outliers and missing data. Furthermore, we recommend various Machine/Deep Learning approaches for feature selection techniques to ensure model robustness, and we highlight the importance of undergoing rigorous internal and external validations of the selected models. Assessing clinical utility and interpretability is often overlooked but fundamental for real-world implementation. In conclusion, we provide the LB scientific community with an AI-based methodological guidance to bridge the two fields and enhance the integrative analysis of LB features. Graphical abstractWorkchart for multiomics integrative studies in the liquid biopsy field. Note: CTCs, circulating tumor cells; ctDNA, circulating tumor-DNA; TEPs, tumor-educated platelets; miRNA, microRNA; cfRNAs, cell-free RNAs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/724535v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1f250b2org.highwire.dtl.DTLVardef@18fe36corg.highwire.dtl.DTLVardef@19c02b9org.highwire.dtl.DTLVardef@176f6e0_HPS_FORMAT_FIGEXP M_FIG C_FIG

As human spaceflight becomes increasingly relevant, understanding how microgravity affects the human brain is an important but largely unexplored question, particularly in the context of neuronal function and vulnerability to neurodegeneration. Direct investigation of these processes in humans is not feasible, necessitating the use of physiologically relevant in vitro model systems. Three-dimensional human brain organoids recapitulate key aspects of brain development and organization and provide an experimentally accessible platform to study neuronal responses under controlled conditions. Here, within the framework of the student competition "Uberflieger 2", we investigated the effects of long-term microgravity on human midbrain organoids cultured for 40 days aboard the International Space Station (ISS). Midbrain organoids reproduce essential features of dopaminergic neuron development and are widely used to model Parkinsons disease and related neurodegenerative processes. To enable spaceflight experiments, we developed and implemented an autonomous culture system adapted to the constraints of the ISS environment. During the mission, a hardware malfunction impaired scheduled medium exchange, introducing an additional metabolic stress condition. Despite these limitations, ISS-cultured organoids remained viable and showed robust neurite outgrowth. Molecular and imaging analyses revealed that exposure to microgravity in combination with nutrient limitation induced a coordinated response involving cytoskeletal remodeling, neuronal plasticity, and selective vulnerability of dopaminergic neurons. These findings demonstrate that human midbrain organoids can maintain key structural and functional properties under prolonged spaceflight-associated stress while activating adaptive response programs. This work highlights the potential of organoid-based systems to investigate neurobiological effects of microgravity and provides a foundation for future studies addressing mechanisms relevant to neurodegenerative disease.

As the duration of space flights increases, so does the need to optimize off-planet microbial growth. Microbes can both be unintentionally brought into space and cause human disease or be intentionally harnessed for on-site bioengineering functions. However, optimizing microbial growth is challenging due to an insufficient understanding of how microbial communities are affected by the extraterrestrial environment. To address this gap, we have modified a previously developed model for cell growth in microgravity. By improving the functional form used for cell growth as well as the code usability, we enable further research into how microbial communities are influenced by gravity. Applying this model to isolate individual effects of gravity on cell growth indicates that a lack of gravity-driven flow decreases cell growth in microgravity, while the absence of sedimentation increases cell growth in microgravity. These opposite effects likely contribute to the system-dependent effects of microgravity observed experimentally.

Delirium, a dynamic neuropsychiatric condition associated with morbidity and mortality, remains underdiagnosed due to reliance on subjective, intermittent screening tools. Objective and potentially continuous identification is needed to improve clinical care. We developed and validated an analytic framework for delirium classification based on automatically extracted video features. In this prospective cohort study, patients ([&ge;] 18 years) admitted to the inpatient medical or neurological ward of a tertiary academic center between August 2020 and March 2022 with an expected stay longer than one night were enrolled. Daily structured delirium assessments and brief video recordings were performed in consenting patients. Videos were analyzed using deep learning pose estimation to extract keypoints and calculate behavioral features based on eye, face, and limb postures and movements. Four machine learning models (logistic regression, gradient boosting, support vector machines, and random forests) were trained to predict delirium status from extracted features. Model performance was evaluated on 20 repetitions of three-fold cross-validation using the area under the curve of the receiver operating characteristics curve (AUC ROC). The cohort included 109 videos from 25 male and 25 female participants (median age: 72, IQR: 63.25-78). Twenty videos (18%) were from patients with delirium. Keypoints for this dataset were more accurately extracted using a customized ResNet-101 model developed with DeepLabCut (sensitivity 0.94, specificity 0.89, compared to human-labeled gold standards) than using off-the-shelf models. Keypoints were then used to generate behavioral features summarizing movement and postures throughout the video. A support vector machine model achieved an average delirium classification AUC ROC of 0.79 (SD {+/-} 0.09), sensitivity of 0.71 (SD {+/-} 0.16), and specificity of 0.78 (SD {+/-} 0.07). This study demonstrates the feasibility of identifying delirium using brief videos in clinically heterogeneous cohorts and reveals novel features for objective identification. Author SummaryDelirium is a sudden change in attention and awareness that commonly affects hospitalized patients. It is linked with longer hospital stays, cognitive decline, and death. Patients with delirium often show changes in movements and behaviors such as slowed movement, restlessness, or excessive scanning of the environment. Since current screening tools rely on intermittent human interactions, they can be subjective and miss the fluctuating nature of delirium, leading to underdiagnosis. We sought to explore whether short video recordings could be used to detect delirium automatically. In our study, we enrolled 50 hospitalized patients and conducted daily delirium assessments and video recordings. We used a machine learning model to analyze patients eye movements, facial expressions, and body postures. We found that video-derived features could be used to identify delirium in a small clinical cohort. While needing further validation in outside cohorts, this study shows an important proof-of-concept for objective delirium monitoring in heterogeneous clinical contexts without adding burden to clinical staff.

Introduction: Infectious and wound-healing complications after colorectal surgery often increase the complexity of local care and the need for specialized enterostomal therapy follow-up after hospital discharge. Despite the growing use of predictive models in digestive surgery, a translational gap remains between perioperative prediction and the practical organization of specialized care. Therefore, the aim of this study was to develop and temporally validate a machine-learning-based risk stratification model to estimate the probability of post-discharge outcomes associated with greater demand for enterostomal therapy after colorectal surgery. Methods: This was a retrospective observational study including 7,908 patients who underwent colorectal surgery between 2005 and 2014. The outcome was defined as the occurrence of superficial surgical site infection, delayed wound healing, or abdominal sinus formation. Routinely available preoperative and intraoperative variables were used as predictors. The primary model was based on gradient boosting with isotonic calibration. Temporal validation was performed by separating cohorts according to year of surgery. Performance was assessed using ROC-AUC, PR-AUC, Brier score, calibration, and decision-oriented clinical metrics. Clinical utility was examined through percentile-based risk stratification and Decision Curve Analysis (DCA). Results: The outcome prevalence in the test set was 6.6%. The calibrated model achieved a ROC-AUC of 0.64 and a PR-AUC of 0.11, with a Brier score of 0.061. The Top-10% risk stratum concentrated approximately twice the baseline event rate ({approx}14% vs. 6.6%), with a number needed for intensified follow-up of 7 patients to identify one event. Decision curve analysis showed greater net benefit than strategies of following all or no patients, particularly for threshold probabilities between 3% and 13%. Models based exclusively on preoperative or intraoperative variables performed worse than the combined model. Conclusion: STOMAPY demonstrated the ability to organize patients along a continuous gradient of risk for post-discharge outcomes associated with greater demand for enterostomal therapy. Although discriminatory performance was moderate, the adequate calibration, temporal validation, and net benefit observed across clinically plausible thresholds support its usefulness as a tool for proportional care prioritization rather than as an individual diagnostic test. Prospective studies and external validations are needed to confirm direct clinical impact.

Rapid and specific diagnosis of viral and bacterial infections is a significant challenge in medicine and veterinary science, especially in the case of epidemically dangerous pathogens. The African swine fever virus (ASFV), for example, causes annual outbreaks among livestock, resulting in significant economic losses for farmers. DNA aptamers have been identified as a promising tool for point-of-care diagnostics, being highly specific to the target and stable ambient temperatures during storage. In this study, we describe the selection of DNA aptamers targeting the p54 viral protein using a single-round selection process. These aptamers were able to bind both to recombinant protein and inactivated ASFV viral particles. Analysis of the newly generated aptamers revealed a dependence of affinity and thermal stability on Ni2+ content, which was a dopant in the selection process. In some cases, the affinity increased 100 times, and melting temperature increased by 30{degrees}C. We have identify two novel DNA motifs that bound 2-3 Ni2+ or Zn2+ ions.

Background: The quality of palliative care in non-designated cancer hospitals, where approximately 70% of deaths of patients with cancer occur, remains unevaluated. This study aimed to clarify the quality of palliative care in these hospitals by comparing patient characteristics and evaluating the quality of palliative care provided by bereaved families. Methods: A questionnaire survey was conducted among bereaved family members of patients with cancer who died in 2018 at designated and non-designated cancer hospitals (excluding palliative care units). We compared the two groups regarding patient and bereaved family characteristics, quality assessment of palliative care (including Memorial Symptom Assessment Scale [MSAS]), care satisfaction, and the presence of end-of-life discussions. Results: In total, 27,944 bereaved family members agreed to participate. The mean age at death was 73.2 ({+/-}11.9) and 79.7 ({+/-}10.9) years for designated and non-designated cancer hospitals, respectively (p < 0.001, Effect Size [ES] = 0.55). The mean MSAS total score (symptom intensity) was significantly higher for designated cancer hospitals than for non-designated cancer hospitals, even after adjusting for patient characteristics (p < 0.001, ES = 0.39). Conversely, the mean adjusted overall satisfaction was significantly higher in non-designated cancer hospitals (p < 0.001, ES = 0.21) than in designated cancer hospitals. Conclusions: Non-designated cancer hospitals had older and less symptomatic patients than designated cancer hospitals. However, there was no significant clinical difference in the quality of palliative care, as assessed by the bereaved families.

Gene model for the ortholog of Lst8 (Lst8) in the May 2011 (WUGSC dyak_caf1/DyakCAF1) Genome Assembly (GenBank Accession: GCA_000005975.1) of Drosophila yakuba. This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus Drosophila using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

Bread wheat (Triticum aestivum) is a staple food crucial for global caloric intake and food security. The current climate emergency demands the development of sustainable agricultural practices, particularly in the context of drought-induced yield reductions in bread wheat. Microalgae-based biostimulants have emerged as promising tools to enhance crop tolerance to drought stress while concurrently mitigating atmospheric CO2 accumulation. This study characterizes the transcriptomic responses to the foliar application of the microalgae-based biostimulant LRMTM in drought-stressed and fully irrigated wheat plants unveiling its mode of action. Drought stress at the tillering stage significantly altered gene expression activating key pathways related to phosphate starvation response (PSR), inositol phosphate signaling, and tocopherol biosynthesis. The application of the microalgae-based biostimulant LRMTM in drought-stressed plants further enhanced the expression of drought-responsive genes, particularly those involved in PSR and carbon fixation. Specific responses to LRMTM treatment in drought-stressed plants were also found related to abscisic acid (ABA) signaling activating genes involved in stomata closure, which plays a critical role in drought tolerance. In fully irrigated plants, LRMTM treatment was also beneficial modulating circadian rhythms, shade avoidance and attenuating stress responses. Phenotypic analysis showed that LRMTM-treated plants exhibited enhanced drought tolerance, increased height and spike length even under fully irrigated conditions. These results indicate that the microalgae-based biostimulant LRMTM not only enhances wheat response to drought but also promotes growth and productivity in both stressed and non-stressed conditions which could contribute to the development of sustainable agriculture in the face of the current climate challenges.

Introduction Obstructive lung diseases (OLDs) are responsible for high rates of illness and death worldwide. Inflammation, chronic airflow limitation, and bronchial remodeling occur in OLD and eventually result in the unique respiratory sounds. Despite its subjective and having low reproducibility, still traditional auscultation using a manual stethoscope is the main method used to identify the lung sounds. Nevertheless, the combination of recent advancements in digital stethoscopes and AI (Artificial Intelligence) has permitted the objective measurement of lung sounds. Nevertheless, there is a lack of standardized, region-specific databases for AI training and validation. Even though lung sound classification is an emerging aspect in research and telerehabilitation the lobar wise acoustic pattern is still novel due to lack of prevailing database to train AI models. Identifying this gap this study aims to develop an acoustic repository and analyze the data using segmental lung sounds from patients with OLDs and healthy controls through an electronic stethoscope. Methods and analysis This is a cross sectional observational study involving 120 participants (60 OLD patients and 60 healthy controls). Lobar wise acoustic signals will be captured using an electronic stethoscope in healthy and diseases population. The data will be analyzed using Audacity software for annotations and then it will be used for feature extraction and statistical analysis. The acoustic features extracted through Audacity, will include frequency, intensity, pitch, and root mean square (RMS) energy. Repeated measures ANOVA will be applied to compare mean sound intensities across lung segments while Pearson correlation will be used to assess associations with body composition parameters. The data will then be standardized for AI-based diagnostic applications. Ethics and dissemination The study is being reviewed from the Ethics Review Committee, Faculty of Medicine, University of Peradeniya (2025/EC/87) will be sought. Informed consent will be obtained in writing. The dissemination of results will take place through peer-reviewed publications and the creation of a public database containing lung sounds from the region.

In many oviparous animals, egg yolk is the sole source of nutrition until feeding begins, and carbohydrates are present in only small amounts in the yolk. Glucose plays an important role in the developmental processes of various animals. In addition, gluconeogenesis has been reported to occur in the yolk syncytial layer (YSL) of cartilaginous fish and teleosts. In contrast, the role of gluconeogenesis in tetrapods remains unclear. In this study, we used Xenopus tropicalis, an anuran amphibian, which lacks YSL, and therefore provide an opportunity to examine the evolutionary conservation of gluconeogenic mechanisms among vertebrates. In X. tropicalis, liquid chromatography/mass spectrometry revealed that glucose levels increased before liver formation. Subsequent tracer experiments using 13C-labeled metabolic substrates detected gluconeogenesis activity from glycerol and lactate. Expression analyses showed that gluconeogenic genes are expressed in the epidermis and endoderm. Consistently, G0 knockout of fbp1, a key gluconeogenic gene, resulted in a significant reduction in glucose levels, affecting brain development. These findings first demonstrate that gluconeogenesis supports development of X. tropicalis. To the best of our knowledge, gluconeogenesis in developing epidermis has not been reported, highlighting previously unrecognized diversity in tissue-specific metabolism during vertebrate development. Comparative analyses across species will provide further insights into the evolution and functional significance of embryonic gluconeogenesis and nutrient metabolism.

Plant disease detection using deep learning is essential for precision agriculture, enabling early and automated crop health monitoring. This study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset. The framework integrates data preprocessing, augmentation, and a VGG-16 backbone with a two-stage fine-tuning strategy. The proposed model is evaluated against CNN, DenseNet-121, Inception-V3, EfficientNetB0, and ResNet-50, achieving an accuracy of 0.93 with precision, recall, and F1-scores of 0.93, 0.90, and 0.92, respectively. These results demonstrate the effectiveness of transfer learning for fine-grained plant disease recognition. We further evaluate model robustness under adversarial cyber attacks to assess deployment reliability in agricultural systems. Under Fast Gradient Sign Method (FGSM) attacks ({epsilon} = 0.01- 0.05), the model shows an accuracy drop of 1%-7.5%, while Projected Gradient Descent (PGD) attacks ({epsilon} = 0.05, step size = 0.005, 10 iterations) produce similar degradation, highlighting the models vulnerability to adversarial perturbations. These findings highlight potential security and reliability risks in AI-based agricultural decision-making systems. Future work will focus on improving robustness and cyber-resilience and extending this framework to other crops for secure and context-aware deployment in resource-constrained environments.

BackgroundEnvironmental conditions shape the evolutionary trajectories of RNA viruses, yet little is known about how complex physical stressors such as microgravity influence host-virus interactions and viral evolution. Here, we investigated the short-term evolutionary consequences of simulated microgravity on the Caenorhabditis elegans - Orsay virus (OrV) system. MethodsOrV was subjected to six serial passages in hosts acclimated to low-shear modeled microgravity, with parallel evolution under standard-gravity. Evolutionary outcomes were evaluated using virulence, transmission, and replication traits, all measured under standard-gravity conditions. ResultsViral load fluctuated across passages in both environments, with lower mean accumulation in microgravity-evolved lineages. After evolution, we detected no significant changes in virulence. Transmission increased in standard-gravity lineages but not in microgravity-evolved ones, while viral replication decreased in all lineages, with a stronger decline in those evolved under microgravity. However, the magnitude of phenotypic changes was generally modest. DiscussionThese results indicate that evolution under microgravity can alter viral phenotypic trajectories over short timescales. However, because all traits were assayed under standard-gravity conditions, we cannot directly assess local adaptation to microgravity, and the observed differences may reflect environment-specific trade-offs rather than reduced fitness per se. Furthermore, the limited number of passages and the modest magnitude of phenotypic change suggest that evolutionary responses may still be in an early stage. ConclusionOverall, our findings provide initial evidence that simulated microgravity can influence the evolutionary dynamics of an RNA virus, while highlighting the need for reciprocal fitness assays and longer-term experiments to fully characterize adaptation to altered gravitational environments.

Sex-related differences in the aging of the human brain were studied using large array of experimental data. The open archive CamCan was used as a source of data: the magnetic encephalograms, co-registered with magnetic resonance images of the head, were obtained for each of 434 subjects (ages 18-87 years, mean age 54.7 {+/-}18.4): 217 females (ages 18-87 years, mean age 54.5 {+/-}18.4) and 217 males (ages 18-84 years, mean age 54.8 {+/-}18.3). Recordings were split in 10-year age cohorts, each cohort consisted of equal number of men and women to calculate average intersex characteristics correctly. By massively solving the inverse problem, functional tomograms were calculated - the spatial distribution of elementary spectral components. Physiological noise was eliminated by joint analysis of MEG-based functional tomogram and magnetic resonance image for each subject. Then multichannel spectra were transformed into time series of the power of elementary current dipoles. Summary electric powers were calculated in six conventional frequency bands (1-4 Hz - delta; 4-8 Hz - theta; 8-13 Hz - alpha; 13-21 Hz - beta1; 21-30 Hz - beta2; 30-48 Hz - gamma), and sex differences in age-related changes were examined. It was found that in the youngest age cohort (18-29 years) the summary electrical power of the brain for males is 1.5 times greater than such power for females. For adults (30-69 years), male and female powers are approximately equal, while in older cohorts (70-87 years), male total brain power is greater. Age dependencies in various frequency bands are generally different for men and women, excluding higher frequencies 21-48 Hz. Basic conclusion can be made that after intersex averaging total electric power of the human brain is invariant through the lifespan from 18 to 87 years. The proposed method of joint MEG and MRI analysis can be used for further study of the sex-related details of brain sources in their connection with age changes.

Vestibular neurons are core elements of the pathways involved in vestibulo-motor functions, such as vestibulo-spinal and vestibulo-ocular reflexes. To meet behavioral needs, electrophysiological neuronal properties are adequately adapted to the sensory-motor computation sustaining these distinct vestibular reflexes. During frog metamorphosis, there is a complete reorganization of the posturo-locomotor system while the oculomotor system remains minimally changed, probably associated to so far unknown changes in vestibular neuronal properties. We used this unique model to investigate the central developmental mechanisms underlying such a reconfiguration of vestibular-associated behaviors. Central vestibular neurons exhibit two types of electrophysiological phenotypes: tonic neurons with a continuous discharge and phasic neurons with a transitory discharge mainly due to the activation of Kv1.1 channel. Electrophysiological recordings and Kv1.1 immunolabeling of vestibulospinal (VS) and vestibulo-ocular (VO) neurons at both larval and juvenile stages revealed that the majority of VS neurons exhibited a tonic discharge in larvae but a phasic discharge in juvenile, while VO neurons remained mainly tonic throughout development. Changes in phasic and tonic neurons proportions in VS population are partly explained by neurogenesis. But we provide evidences that an electrophysiological phenotype switch is a concomitant developmental mechanism participating in the maturation of these central vestibular neurons. All together our results showed that the maturation process in central vestibular neuronal groups is highly related to the metamorphosis-induced remodeling of vestibulo-motor functions they are involved in, with the ultimate purpose of ensuring an adequate adaptation of neuronal elements properties to the developmental changes of behavioral constrains.

{gamma}-Glutamyl cyclotransferases (GGCTs) belongs to class of cytosolic enzymes that are responsible for glutathione (GSH) degradation under stress conditions. They regulate GSH homeostasis through the {gamma}-glutamyl cycle which is responsible for maintaining the synthesis of GSH as well as its breakdown, enabling recycling of its constituent amino acids. Although GGCTs have been implicated in enhancing heavy metal (HMs) tolerance in plants, their role in biotic stress remains largely unexplored. Previously, OsGGCT1 was identified as a gene strongly upregulated in Fusarium stress. In this study, the GGCT1 homolog from Oryza sativa japonica was characterized for its role in conferring tolerance to Fusarium oxysporum (F.O.). Similar to abiotic factors, biotic stresses significantly impact crop yield and productivity. The rhizosphere harbors diverse microbial communities, including harmful pathogens such as F. oxysporum. Fusarium causes wilt disease in a variety of plant species, such as: tomato, legumes, rice, and Arabidopsis thaliana. Our results demonstrate that overexpression of OsGGCT1 enhanced tolerance to F. oxysporum in A. thaliana, primarily by reducing fungal spore accumulation. Transgenic plants showed elevated expression of OsGGCT1 along with AtGSH1 and AtGSH2, reduced levels of reactive oxygen species (ROS), improved growth and photosynthetic performance and enhanced activities of the antioxidant enzymes. OsGGCT1 serves as a key component in maintaining GSH homeostasis by supporting glutamate (Glu) regeneration necessary for sustained GSH biosynthesis. Overall, these findings identify OsGGCT1 as an important constituent of the GSH-mediated detoxification pathway against Fusarium oxysporum and provide valuable molecular insights for developing Fusarium-tolerant rice varieties with reduced fungal accumulation.

Salinity is a major crop production constraint in dry pea (Pisum sativum L.), making the development of salt-tolerant varieties essential to improve crop productivity and land-use efficiency. The genetic mechanisms of salt tolerance in dry pea is largely unknown, and research on salt-tolerant genes is limited. In this study, we established comprehensive genomic and transcriptomic resources, along with a robust screening protocol, to dissect the genetic basis of salinity tolerance using two germplasm sets: the USDA pea diversity panel, consisting of approximately 200 globally sourced accessions, and a set of 300 modern elite lines from the NDSU Pulse Crops Breeding Program. Genetic variation for the salinity response was assessed based on ten phenotypic traits, with root dry weight, shoot dry weight, and specific root length identified as key indicators based on their heritability. Genome-wide association mapping uncovered significant genomic regions and several candidate genes linked to salt stress, with the strongest association found on chromosome 6. Overlapping QTL signals across traits suggest a shared genetic architecture underlying salinity tolerance. Field-based transcriptomic analysis further identified five putative genes involved in salinity response conserved across multiple crop species. Notably, Psat5g000800, encoding a glycosyl hydrolase gene, was markedly upregulated under salinity stress. These findings highlight the complex, multi-gene regulatory nature of salinity tolerance in dry pea and underscore the importance of functional validation of candidate genes. This study provides key insights and practical tools to support breeding efforts aimed at improving salt tolerance in dry pea.

The NASA Rodent Habitat aboard the International Space Station enabled long-duration studies of behavioral responses to spaceflight, but video-based behavioral analysis has relied on laborious manual annotation. No study has tested whether deep learning tools can automate this analysis under the demanding imaging conditions of orbital vivaria. We applied pose estimation (SLEAP) and behavioral segmentation (DeepEthogram) to archival footage from the Rodent Research-1 mission. Nine labelers annotated 3,249 pose labels across 2,063 frames, and three behaviorists labeled 411,194 frames across 66 videos. Pose tracking accuracy approximated human inter-annotator variability despite progressive lens soiling, grid occlusions, and spherical aberration. Behavioral classification across eight categories achieved accuracy of 0.86-0.90 and suggests progressive behavioral adaptations to microgravity. Kinematic reconstruction of circling estimated centripetal accelerations periodically approaching 1g. This is the first application of deep learning-based pose estimation and behavioral segmentation to rodents in spaceflight, establishing benchmarks for future monitoring systems.