Am J Physiol Lung Cell Mol Physiol. 2025 Mar 17. doi: 10.1152/ajplung.00258.2024. Online ahead of print.

ABSTRACT

Cigarette smoke (CS) is a leading cause of chronic obstructive pulmonary disease (COPD). Here, we investigated whether the ion channel amplifier nesolicaftor rescues CS-induced mucociliary and ion channel dysfunction. Since CS increases expression of transforming growth factor-beta1 (TGF-β1), human bronchial epithelial cells (HBEC) from healthy donors were used for TGF-β1 and COPD donors (COPD-HBEC) for CS exposure experiments. CS and TGF-β1 induce mucociliary dysfunction by increasing MUC5AC and decreasing ion channel conductance important for mucus hydration. These include cystic fibrosis transmembrane conductance regulator (CFTR) and apical large-conductance, Ca2+-activated K+ (BK) channels. Nesolicaftor rescued CFTR and BK channel dysfunction, restored ciliary beat frequency (CBF), and decreased mucus viscosity and MUC5AC expression in CS-exposed COPD-HBEC. Nesolicaftor further reversed reductions in ASL volumes, CBF, and CFTR and BK conductance, and blocked the increase in extracellular signal-regulated kinase (ERK) signaling in TGF-β1-exposed normal HBEC. Mechanistically, nesolicaftor increased, as expected, binding of PCBP1 to CFTR mRNA, but surprisingly also to LRRC26 mRNA, which encodes the gamma subunit required for BK function. Similar to nesolicaftor, the angiotensin receptor blocker (ARB) losartan rescued TGF-β1-mediated decreases in PCBP1 binding to LRRC26 mRNA. In addition, the ARB telmisartan restored PCBP1 binding to CFTR and LRRC26 mRNAs to rescue CFTR and BK function in CS-exposed COPD-HBEC. Thus, nesolicaftor and ARBs act on the same target and were therefore neither additive nor synergistic in their actions. These data demonstrate that nesolicaftor and ARBs may provide benefits in COPD by improving ion channel function important for mucus hydration.

PMID:40095970 | DOI:10.1152/ajplung.00258.2024

Pediatr Nephrol. 2025 Mar 17. doi: 10.1007/s00467-025-06715-3. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is a life-shortening multisystem disease resulting from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, causing the most devastating phenotypes in the airway and pancreas. Significant advances in treatment for CF lung disease, including the expanded use of high-efficiency modulator therapies (HEMT) such as Trikafta, have dramatically increased both quality of life and life expectancy for people with CF (PwCF). With these advances, long-term extrapulmonary manifestations are more frequently recognized. Pseudo-Barter syndrome, acute kidney injury (AKI) induced by medications or dehydration, amyloidosis, nephrolithiasis, and IgA and diabetic nephropathies have been previously reported in PwCF. Newer data suggest that chronic kidney disease (CKD) is a new morbidity in the aging CF population, affecting 19% of people over age 55. CKD carries a high risk of premature death from cardiovascular complications. Studies suggest that CFTR dysfunction increases kidneys' vulnerability to injury caused by the downstream effects of CF. Improving the mutant CFTR function by HEMT may help to tease apart the kidney responses resulting from extrinsic factors and those intrinsically related to the CFTR gene mutations. Additionally, given the novelty of HEMT approaches, the potential off-target effects of their long-term use are currently unknown. We review the evolving kidney complications in PwCF and propose the term CF-related kidney disease. We hope this review will increase awareness about the changing phenotype of kidney dysfunction in PwCF and help prevent morbidity related to this condition.

PMID:40095037 | DOI:10.1007/s00467-025-06715-3

Comput Methods Biomech Biomed Engin. 2025 Mar 17:1-18. doi: 10.1080/10255842.2025.2477801. Online ahead of print.

ABSTRACT

Electroencephalography (EEG) signals are widely employed due to their spontaneity and robustness against artifacts in emotion recognition. However, existing methods are often unable to fully integrate high-dimensional features and capture changing patterns in time series when processing EEG signals, which results in limited classification performance. This paper proposes an emotion recognition method (AEF-DL) based on autoencoder fusion features and MSC-TimesNet models. Firstly, we segment the EEG signal in five frequency bands into time windows of 0.5 s, extract power spectral density (PSD) features and differential entropy (DE) features, and implement feature fusion using the autoencoder to enhance feature representation. Based on the TimesNet model and incorporating the multi-scale convolutional kernels, this paper proposes an innovative deep learning model (MSC-TimesNet) for processing fused features. MSC-TimesNet efficiently extracts inter-period and intra-period information. To validate the performance of the proposed method, we conducted systematic experiments on the public datasets DEAP and Dreamer. In dependent experiments with subjects, the classification accuracies reached 98.97% and 95.71%, respectively; in independent experiments with subjects, the accuracies reached 97.23% and 92.95%, respectively. These results demonstrate that the proposed method exhibits significant advantages over existing methods, highlighting its effectiveness and broad applicability in emotion recognition tasks.

PMID:40096584 | DOI:10.1080/10255842.2025.2477801

Magn Reson Med. 2025 Mar 17. doi: 10.1002/mrm.30447. Online ahead of print.

ABSTRACT

PURPOSE: Dynamic glucose enhanced (DGE) MRI studies employ CEST or spin lock (CESL) to study glucose uptake. Currently, these methods are hampered by low effect size and sensitivity to motion. To overcome this, we propose to utilize exchange-based linewidth (LW) broadening of the direct water saturation (DS) curve of the water saturation spectrum (Z-spectrum) during and after glucose infusion (DS-DGE MRI).

METHODS: To estimate the glucose-infusion-induced LW changes (ΔLW), Bloch-McConnell simulations were performed for normoglycemia and hyperglycemia in blood, gray matter (GM), white matter (WM), CSF, and malignant tumor tissue. Whole-brain DS-DGE imaging was implemented at 3 T using dynamic Z-spectral acquisitions (1.2 s per offset frequency, 38 s per spectrum) and assessed on four brain tumor patients using infusion of 35 g of D-glucose. To assess ΔLW, a deep learning-based Lorentzian fitting approach was used on voxel-based DS spectra acquired before, during, and post-infusion. Area-under-the-curve (AUC) images, obtained from the dynamic ΔLW time curves, were compared qualitatively to perfusion-weighted imaging parametric maps.

RESULTS: In simulations, ΔLW was 1.3%, 0.30%, 0.29/0.34%, 7.5%, and 13% in arterial blood, venous blood, GM/WM, malignant tumor tissue, and CSF, respectively. In vivo, ΔLW was approximately 1% in GM/WM, 5% to 20% for different tumor types, and 40% in CSF. The resulting DS-DGE AUC maps clearly outlined lesion areas.

CONCLUSIONS: DS-DGE MRI is highly promising for assessing D-glucose uptake. Initial results in brain tumor patients show high-quality AUC maps of glucose-induced line broadening and DGE-based lesion enhancement similar and/or complementary to perfusion-weighted imaging.

PMID:40096575 | DOI:10.1002/mrm.30447

Magn Reson Med. 2025 Mar 17. doi: 10.1002/mrm.30473. Online ahead of print.

ABSTRACT

PURPOSE: Trityl OXO71-based pulse electron paramagnetic resonance imaging (EPRI) is an excellent technique to obtain partial pressure of oxygen (pO2) maps in tissues. In this study, we used deep learning techniques to denoise 3D EPR amplitude and pO2 maps.

METHODS: All experiments were performed using a 25 mT EPR imager, JIVA-25®. The MONAI implementation of four neural networks (autoencoder, Attention UNet, UNETR, and UNet) was tested, and the best model (UNet) was then enhanced with joint bilateral filters (JBF). The training dataset was comprised of 227 3D images (56 in vivo and 171 in vitro), 159 images for training, 45 for validation, and 23 for testing. UNet with 1, 2, and 3 JBF layers was tested to improve image SNR, focusing on multiscale structural similarity index measure and edge sensitivity preservation. The trained algorithm was tested using acquisitions with 15, 30, and 150 averages in vitro with a sealed deoxygenated OXO71 phantom and in vivo with fibrosarcoma tumors grown in a hind leg of C3H mice.

RESULTS: We demonstrate that UNet with 2 JBF layers (UNet+JBF2) provides the best outcome. We demonstrate that using the UNet+JBF2 model, the SNR of 15-shot amplitude maps provides higher SNR compared to 150-shot pre-filter maps, both in phantoms and in tumors, therefore, allowing 10-fold accelerated imaging. We demonstrate that the trained algorithm improves SNR in pO2 maps.

CONCLUSIONS: We demonstrate the application of deep learning techniques to EPRI denoising. Higher SNR will bring the EPRI technique one step closer to clinics.

PMID:40096518 | DOI:10.1002/mrm.30473

Sensors (Basel). 2025 Mar 6;25(5):1635. doi: 10.3390/s25051635.

ABSTRACT

Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores-95.3% and 89.5%, respectively-surpassing previous benchmarks. Additionally, we tested the model's transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management.

PMID:40096500 | DOI:10.3390/s25051635

Sensors (Basel). 2025 Mar 5;25(5):1594. doi: 10.3390/s25051594.

ABSTRACT

Cloud gaming has rapidly transformed the gaming industry, allowing users to play games on demand from anywhere without the need for powerful hardware. Cloud service providers are striving to enhance user Quality of Experience (QoE) using traditional assessment methods. However, these traditional methods often fail to capture the actual user QoE because some users are not serious about providing feedback regarding cloud services. Additionally, some players, even after receiving services as per the Service Level Agreement (SLA), claim that they are not receiving services as promised. This poses a significant challenge for cloud service providers in accurately identifying QoE and improving actual services. In this paper, we have compared our previous proposed novel technique that utilizes a deep learning (DL) model to assess QoE through players' facial expressions during cloud gaming sessions in a virtual reality (VR) environment. The EmotionNET model technique is based on a convolutional neural network (CNN) architecture. Later, we have compared the EmotionNET technique with three other DL techniques, namely ConvoNEXT, EfficientNET, and Vision Transformer (ViT). We trained the EmotionNET, ConvoNEXT, EfficientNET, and ViT model techniques on our custom-developed dataset, achieving 98.9% training accuracy and 87.8% validation accuracy with the EmotionNET model technique. Based on the training and comparison results, it is evident that the EmotionNET model technique predicts and performs better than the other model techniques. At the end, we have compared the EmotionNET results on two network (WiFi and mobile data) datasets. Our findings indicate that facial expressions are strongly correlated with QoE.

PMID:40096493 | DOI:10.3390/s25051594

Sensors (Basel). 2025 Mar 6;25(5):1622. doi: 10.3390/s25051622.

ABSTRACT

The Landsat archive stands as one of the most critical datasets for studying landscape change, offering over 50 years of imagery. This invaluable historical record facilitates the monitoring of land cover and land use changes, helping to detect trends in and the dynamics of the Earth's system. However, the relatively low temporal frequency and irregular clear-sky observations of Landsat data pose significant challenges for multi-temporal analysis. To address these challenges, this research explores the application of a closed-form continuous-depth neural network (CFC) integrated within a recurrent neural network (RNN) called CFC-mmRNN for reconstructing historical Landsat time series in the Canadian Prairies region from 1985 to present. The CFC method was evaluated against the continuous change detection (CCD) method, widely used for Landsat time series reconstruction and change detection. The findings indicate that the CFC method significantly outperforms CCD across all spectral bands, achieving higher accuracy with improvements ranging from 33% to 42% and providing more accurate dense time series reconstructions. The CFC approach excels in handling the irregular and sparse time series characteristic of Landsat data, offering improvements in capturing complex temporal patterns. This study underscores the potential of leveraging advanced deep learning techniques like CFC to enhance the quality of reconstructed satellite imagery, thus supporting a wide range of remote sensing (RS) applications. Furthermore, this work opens up avenues for further optimization and application of CFC in higher-density time series datasets such as MODIS and Sentinel-2, paving the way for improved environmental monitoring and forecasting.

PMID:40096481 | DOI:10.3390/s25051622

Sensors (Basel). 2025 Mar 5;25(5):1589. doi: 10.3390/s25051589.

ABSTRACT

The health status of rotating machinery equipment in nuclear power plants is of paramount importance for ensuring the overall normal operation of the power plant system. In particular, significant failures in large rotating machinery equipment, such as main pumps, pose critical safety hazards to the system. Therefore, this paper takes pump equipment as a representative of rotating machinery in nuclear power plants and proposes a fault diagnosis method based on a multi-scale convolutional self-attention network for three types of faults: outer ring fracture, inner ring fracture, and rolling element pitting corrosion. Within the multi-scale convolutional self-attention network, a multi-scale hybrid feature complementarity mechanism is introduced. This mechanism leverages an adaptive encoder to capture deep feature information from the acoustic signals of rolling bearings and constructs a hybrid-scale feature set based on deep features and original signal characteristics in the time-frequency domain. This approach enriches the fault information present in the feature set and establishes a nonlinear mapping relationship between fault features and rolling bearing faults. The results demonstrate that, without significantly increasing model complexity or the volume of feature data, this method achieves a substantial increase in fault diagnosis accuracy, exceeding 99.5% under both vibration signal and acoustic signal conditions.

PMID:40096472 | DOI:10.3390/s25051589

Sensors (Basel). 2025 Mar 6;25(5):1615. doi: 10.3390/s25051615.

ABSTRACT

E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals and real-time responses. Recently, deep learning techniques, such as the convolutional neural network, recurrent neural network, and transformer methods, provide effective solutions that can automatically extract data features and recognize patterns, significantly improving the analysis of e-skin data. Deep learning is not only capable of handling multimodal data but can also provide real-time response and personalized predictions in dynamic environments. Nevertheless, problems such as insufficient data annotation and high demand for computational resources still limit the application of e-skin. Optimizing deep learning algorithms, improving computational efficiency, and exploring hardware-algorithm co-designing will be the key to future development. This review aims to present the deep learning techniques applied in e-skin and provide inspiration for subsequent researchers. We first summarize the sources and characteristics of e-skin data and review the deep learning models applicable to e-skin data and their applications in data analysis. Additionally, we discuss the use of deep learning in e-skin, particularly in health monitoring and human-machine interactions, and we explore the current challenges and future development directions.

PMID:40096464 | DOI:10.3390/s25051615

Sensors (Basel). 2025 Mar 4;25(5):1578. doi: 10.3390/s25051578.

ABSTRACT

This study proposes an enhanced network intrusion detection model, 1D-TCN-ResNet-BiGRU-Multi-Head Attention (TRBMA), aimed at addressing the issues of incomplete learning of temporal features and low accuracy in the classification of malicious traffic found in existing models. The TRBMA model utilizes Temporal Convolutional Networks (TCNs) to improve the ResNet18 architecture and incorporates Bidirectional Gated Recurrent Units (BiGRUs) and Multi-Head Self-Attention mechanisms to enhance the comprehensive learning of temporal features. Additionally, the ResNet network is adapted into a one-dimensional version that is more suitable for processing time-series data, while the AdamW optimizer is employed to improve the convergence speed and generalization ability during model training. Experimental results on the CIC-IDS-2017 dataset indicate that the TRBMA model achieves an accuracy of 98.66% in predicting malicious traffic types, with improvements in precision, recall, and F1-score compared to the baseline model. Furthermore, to address the challenge of low identification rates for malicious traffic types with small sample sizes in unbalanced datasets, this paper introduces TRBMA (BS-OSS), a variant of the TRBMA model that integrates Borderline SMOTE-OSS hybrid sampling. Experimental results demonstrate that this model effectively identifies malicious traffic types with small sample sizes, achieving an overall prediction accuracy of 99.88%, thereby significantly enhancing the performance of the network intrusion detection model.

PMID:40096461 | DOI:10.3390/s25051578

Sensors (Basel). 2025 Mar 4;25(5):1574. doi: 10.3390/s25051574.

ABSTRACT

Face recognition (FR) is a less intrusive biometrics technology with various applications, such as security, surveillance, and access control systems. FR remains challenging, especially when there is only a single image per person as a gallery dataset and when dealing with variations like pose, illumination, and occlusion. Deep learning techniques have shown promising results in recent years using VAE and GAN, with approaches such as patch-VAE, VAE-GAN for 3D Indoor Scene Synthesis, and hybrid VAE-GAN models. However, in Single Sample Per Person Face Recognition (SSPP FR), the challenge of learning robust and discriminative features that preserve the subject's identity persists. To address these issues, we propose a novel framework called AD-VAE, specifically for SSPP FR, using a combination of variational autoencoder (VAE) and Generative Adversarial Network (GAN) techniques. The proposed AD-VAE framework is designed to learn how to build representative identity-preserving prototypes from both controlled and wild datasets, effectively handling variations like pose, illumination, and occlusion. The method uses four networks: an encoder and decoder similar to VAE, a generator that receives the encoder output plus noise to generate an identity-preserving prototype, and a discriminator that operates as a multi-task network. AD-VAE outperforms all tested state-of-the-art face recognition techniques, demonstrating its robustness. The proposed framework achieves superior results on four controlled benchmark datasets-AR, E-YaleB, CAS-PEAL, and FERET-with recognition rates of 84.9%, 94.6%, 94.5%, and 96.0%, respectively, and achieves remarkable performance on the uncontrolled LFW dataset, with a recognition rate of 99.6%. The AD-VAE framework shows promising potential for future research and real-world applications.

PMID:40096455 | DOI:10.3390/s25051574

J Clin Med. 2025 Mar 3;14(5):1693. doi: 10.3390/jcm14051693.

ABSTRACT

Background: Patients suffering from idiopathic pulmonary fibrosis (IPF) may experience acute exacerbation (AE-IPF), which frequently results in acute respiratory failure (ARF) requiring hospitalization. Objective: This study aims to determine if survival has improved over the last decade in patients hospitalized for ARF consequent to AE-IPF, in view of the progress recently made in pharmacological and supportive treatment strategies. Methods: This was an observational retrospective single-center study. The data of 14 patients admitted to an Intermediate Respiratory Care Unit (IRCU) between 1 January 2004 and 31 December 2013 (group A) were compared with those of 26 patients admitted between 1 January 2014 and 31 December 2023 (group B). This study's primary endpoint was survival following IRCU admission. Results: Survival time was significantly longer in the second group of patients compared to the first one [median survival time: 134 (31-257) vs. 25.5 (20-50) days; p < 0.001]. Group B patients also had a lower IRCU mortality rate (6/26 vs. 10/14; p = 0.003) and a significantly shorter stay in the IRCU [6 (1-60) vs. 14 (1-43) days; p = 0.039]. Conclusions: Innovative pharmacologic treatments and supportive therapeutic strategies are able to prolong survival and reduce the risk of in-hospital mortality in patients with AE-IPF hospitalized for ARF.

PMID:40095670 | DOI:10.3390/jcm14051693

J Epidemiol Glob Health. 2025 Mar 17;15(1):44. doi: 10.1007/s44197-025-00377-y.

ABSTRACT

BACKGROUND: Advances in the understanding of idiopathic pulmonary fibrosis (IPF) and international cooperation have led to the publication and subsequent updates of international practice guidelines. The impact of these guidelines, especially significant updates occurring after 2011, on IPF epidemiology and clinical practices remains relatively unexplored.

METHODS: This retrospective nationwide population-based study utilized the Whole-Population Datafiles (WPD) of Taiwan's National Health Insurance Research Database that contained basic demographics, complete claim data, and causes of death for all insured persons. We refined the code-based definition to identify IPF cases from the WPD between 2011 and 2019. Independent validation confirmed the high accuracy of this definition. We analyzed the annual standardized rates of IPF incidence, prevalence, overall and IPF-specific all-cause mortality. Additionally, we examined trends in the prescription of selected medications and the proportions of patients with respiratory failure receiving invasive (IMV) and non-invasive (NIV) mechanical ventilation.

RESULTS: We included 4359 incident cases of IPF. From 2011 to 2019, the annual standardized incidence rates increased from 1.66 (95% confidence interval [CI], 1.36-1.97) to 11.35 (95% CI, 10.65-12.04) per 100,000 standard population, and the annual standardized prevalence rates increased from 1.98 (95% CI, 1.65-2.31) to 27.25 (95% CI, 26.17-28.33) per 100,000 standard population. The standardized IPF-specific all-cause mortality and respiratory failure rates remained stable. Male and older patients received IPF diagnoses more frequently, and experienced higher mortality rates, compared to their female and younger counterparts. Most deaths were attributed to respiratory causes, without significant seasonal variation. Changing trends in the management of IPF mirrored with the evolving guideline recommendations, and showed diminishing roles of immunosuppressants, growing usage of antifibrotics, and NIV usage surpassing IMV.

CONCLUSIONS: Our findings reflected the longitudinal impact of the recently evolving guideline recommendations on IPF epidemiology and real-world management.

PMID:40095261 | DOI:10.1007/s44197-025-00377-y

J Clin Med. 2025 Feb 24;14(5):1496. doi: 10.3390/jcm14051496.

ABSTRACT

Telomeropathies, or telomere biology disorders (TBDs), are syndromes that can cause a number of medical conditions, including interstitial lung disease (ILD), bone marrow failure, liver fibrosis, and other diseases. They occur due to genetic mutations to the telomerase complex enzymes that result in premature shortening of telomeres, the caps on the ends of cellular DNA that protect chromosome length during cell division, leading to early cell senescence and death. Idiopathic pulmonary fibrosis (IPF) is the most common manifestation of the telomere biology disorders, although it has been described in other interstitial lung diseases as well, such as rheumatoid arthritis-associated ILD and chronic hypersensitivity pneumonitis. Telomere-related mutations can be inherited or can occur sporadically. Identifying these patients and offering genetic counseling is important because telomerapathies have been associated with poorer outcomes including death, lung transplantation, hospitalization, and FVC decline. Additionally, treatment with immunosuppressants has been shown to be associated with worse outcomes. Currently, there is no specific treatment for TBD except to transplant the organ that is failing, although there are a number of promising treatment strategies currently under investigation. Shortened telomere length is routinely discovered in patients undergoing lung transplantation for IPF. Testing to detect early TBD in patients with suggestive signs or symptoms can allow for more comprehensive treatment and multidisciplinary care pre- and post-transplant. Patients with TBD undergoing lung transplantation have been reported to have both pulmonary and extrapulmonary complications at a higher frequency than other lung transplant recipients, such as graft-specific complications, increased infections, and complications related to immunosuppressive therapy.

PMID:40095034 | DOI:10.3390/jcm14051496

Aging (Albany NY). 2025 Mar 12;17. doi: 10.18632/aging.206220. Online ahead of print.

ABSTRACT

The dynamic nature of epigenetic modifications has been leveraged to construct epigenetic clocks that accurately predict an individual's age based on DNA methylation levels. Here we explore whether the accumulation of epimutations, which can be quantified by Shannon's entropy, changes reproducibly with age. Using targeted bisulfite sequencing, we analyzed the associations between age, entropy, and methylation levels in human buccal swab samples. We find that epigenetic clocks based on the entropy of methylation states predict chronological age with similar accuracy as common approaches that are based on methylation levels of individual cytosines. Our approach suggests that across many genomic loci, methylation entropy changes reproducibly with age.

PMID:40096548 | DOI:10.18632/aging.206220

Plant Cell. 2025 Mar 17:koaf055. doi: 10.1093/plcell/koaf055. Online ahead of print.

ABSTRACT

Photosystem (PS) II assembly requires auxiliary factors, including Psb28. Although the absence of Psb28 in cyanobacteria has little effect on PSII assembly, we show here that the Chlamydomonas (Chlamydomonas reinhardtii) psb28 null mutant is severely impaired in PSII assembly, showing drastically reduced PSII supercomplexes, dimers and monomers, while overaccumulating early PSII assembly intermediates reaction center II (RCII), CP43mod and D1mod. The mutant had less PSI and more cytochrome b6f complex, its thylakoids were organized mainly as monolayers and it had a distorted chloroplast morphology. Complexome profiling of the psb28 mutant revealed that THYLAKOID ENRICHED FRACTION 5 (TEF5), the homolog of Arabidopsis (Arabidopsis thaliana) PHOTOSYSTEM B PROTEIN 33 (PSB33)/LIGHT HARVESTING-LIKE 8 (LIL8), co-migrated particularly with RCII. TEF5 also interacted with PSI. A Chlamydomonas tef5 null mutant was severely impaired in PSII assembly and overaccumulated RCII and CP43mod. RC47 was not detectable in the light-grown tef5 mutant. Our data suggest a possible role for TEF5 in RCII photoprotection or maturation. Both the psb28 and tef5 mutants exhibited decreased synthesis of CP47 and PsbH, suggesting negative feedback regulation possibly exerted by the accumulation of RCII and/or CP43mod in both mutants. The strong effects of missing auxiliary factors on PSII assembly in Chlamydomonas suggest a more effective protein quality control system in this alga than in land plants and cyanobacteria.

PMID:40096524 | DOI:10.1093/plcell/koaf055

Elife. 2025 Mar 17;14:RP103877. doi: 10.7554/eLife.103877.

ABSTRACT

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (1) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct Escherichia coli promoters and (2) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

PMID:40095799 | DOI:10.7554/eLife.103877

Plant Biol (Stuttg). 2025 Mar 17. doi: 10.1111/plb.70012. Online ahead of print.

ABSTRACT

Iron (Fe) is essential for plants and humans, with over 2 billion people suffering deficiency disorders because most plant foods, including cereals, are low in Fe. Durum wheat, a staple crop in Mediterranean regions, is facing increased droughts, which reduce plant yield and ability to acquire and use Fe. Therefore, understanding mechanisms underlying Fe acquisition and accumulation in durum wheat under drought is essential for both agronomic and nutritional purposes. Here, a durum wheat (Triticum turgidum subsp. durum) genotype with a purple grain pericarp was grown hydroponically under adequate (80 μM) or limited (10 μM) Fe, with or without water stress (induced with 10% PEG 6000) for 6 days. Fe accumulation decreased under Fe deficiency and drought, with the highest phytosiderophore (PS) release in Fe-deficient plants. Interestingly, despite adequate Fe availability, drought inhibited Fe accumulation in roots. This response was accompanied by increased release of PS from roots, although the increase was less than that observed with single or combined Fe deficiency. Both TdIRT1 and TdYS15 were upregulated by Fe deficiency but downregulated by drought and the combined stress. Drought stress and Fe deficiency led to increased ABA production, being 250-fold higher with respect to controls. TdIRT1 downregulation in plants exposed to the combined stress suggests a trade-off between water and Fe stress responses. Our findings demonstrate that the response to combined stress differs from, and is rarely additive to, the response to a single stressor, reinforcing the complexity of plant adaptation to combined environmental stresses.

PMID:40095748 | DOI:10.1111/plb.70012

FASEB J. 2025 Mar 31;39(6):e70419. doi: 10.1096/fj.202401661RRR.

ABSTRACT

Zinc finger-containing ubiquitin peptidase 1 (ZUP1) is a protein characterized by four N-terminal zinc finger domains and a C-terminal deubiquitinase (DUB) domain. While it is associated with the DNA damage response, the role of ZUP1 in innate immunity remains unclear. Here, we identify ZUP1 as a crucial component of the mitochondrial antiviral signaling (MAVS) complex, essential for host antiviral defense. We show that viral infection significantly upregulates ZUP1 expression, and mice lacking ZUP1 exhibit impaired type I interferon (IFN) production and increased susceptibility to viral infection, as evidenced by higher mortality rates. This underscores the protective role of ZUP1 in host immunity. Mechanistically, ZUP1 binds to MAVS through its C-terminal domain independently of DUB activity. Instead, ZUP1 utilizes its zinc finger domains, particularly the third zinc finger, to directly bind viral RNA. This interaction enhances the association of ZUP1 with MAVS and promotes its aggregation on mitochondria during viral infection. ZUP1 also interacts with TBK1 and NEMO within the MAVS complex, facilitating IRF3 activation and type I IFN production. These findings establish ZUP1 as a zinc finger-containing regulator that amplifies MAVS-dependent antiviral immunity, linking viral RNA recognition to downstream signaling and highlighting potential targets for therapeutic intervention against viral infections.

PMID:40095368 | DOI:10.1096/fj.202401661RRR