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Retrograde clay mineral reactions (reverse weathering), including glauconite formation, are first-order controls on element sequestration in marine sediments. Here, we report substantial element sequestration by glauconite formation in shallow marine settings from the Triassic to the Holocene, averaging 3 ± 2 mmol·cm − ²·kyr −1 for K, Mg and Al, 16 ± 9 mmol·cm − ²·kyr −1 for Si and 6 ± 3 mmol·cm − ²·kyr −1 for Fe, which is ~2 orders of magnitude higher than estimates for deep-sea settings. Upscaling of glauconite abundances in shallow-water (0–200 m) environments predicts a present-day global uptake of ~≤ 0.1 Tmol·yr −1 of K, Mg and Al, and ~0.1–0.4 Tmol·yr −1 of Fe and Si, which is ~half of the estimated Mesozoic elemental flux. Clay mineral authigenesis had a large impact on the global marine element cycles throughout Earth’s history, in particular during ‘greenhouse’ periods with sea level highstand, and is key for better understanding past and present geochemical cycling in marine sediments. Here the authors show that reverse weathering reactions, such as the formation of glauconite minerals, are first-order controls on element sequestration in shallow marine sediments throughout Earth history, in particular during greenhouse periods with sea level highstand.

Endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) signaling have been shown to be dysregulated in multiple cancer types. Glucose regulatory protein 78 (GRP78), the master regulator of the UPR, plays a role in proliferation, invasion, and metastasis in cancer. Cancer stem cells (CSCs) make up a crucial component of the tumor heterogeneity in pancreatic cancer, as well as other cancers. “Stemness” in pancreatic cancer defines a population of cells within the tumor that have increased therapeutic resistance as well as survival advantage. In the current study, we investigated how GRP78 was responsible for maintaining “stemness” in pancreatic cancer thereby contributing to its aggressive biology. We determined that GRP78 downregulation decreased clonogenicity and self-renewal properties in pancreatic cancer cell lines in vitro. In vivo studies resulted in delayed tumor initiation frequency, as well as smaller tumor volume in the shGRP78 groups. Additionally, downregulation of GRP78 resulted in dysregulated fatty acid metabolism in pancreatic tumors as well as the cells. Further, our results showed that shGRP78 dysregulates multiple transcriptomic and proteomic pathways that involve DNA damage, oxidative stress, and cell death, that were reversed upon treatment with a ROS inhibitor, N-acetylcysteine. This study thus demonstrates for the first time that the heightened UPR in pancreatic cancer may be responsible for maintenance of the “stemness” properties in these cells that are attributed to aggressive properties like chemoresistance and metastasis.

Resistance to therapy is one of the major factors that contribute to dismal survival statistics in pancreatic cancer. While there are many tumor intrinsic and tumor microenvironment driven factors that contribute to therapy resistance, whether pre-existing metabolic diseases like type 2 diabetes (T2D) contribute to this has remained understudied. It is well accepted that hyperglycemia associated with type 2 diabetes changes the gut microbiome. Further, hyperglycemia also enriches for a “stem-like” population within the tumor. In the current study, we observed that in a T2D mouse model, the microbiome changed significantly as the hyperglycemia developed in these animals. Our results further showed that, tumors implanted in the T2D mice responded poorly to gemcitabine/paclitaxel (Gem/Pac) standard of care compared to those in the control group. A metabolomic reconstruction of the WGS of the gut microbiota further revealed that an enrichment of bacterial population involved in drug metabolism in the T2D group. Additionally, we also observed an increase in the CD133+ tumor cells population in the T2D model. These observations indicated that in an animal model for T2D, microbial dysbiosis is associated with increased resistance to chemotherapeutic compounds.

Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.

The composition of marine authigenic iron silicates bears subtle evidence of paleo-oceanographic and paleoclimatic conditions. This study focuses on the compositionally unique glauconite within the Early Cretaceous Pariwar and Habur formations in the Jaisalmer Basin and discusses its implications. The Al-glauconite is enriched in TFe 2 O 3 (> 18 wt%), and is considered as Fe-rich Al-glauconite. The X-ray diffraction parameters, the micro-texture, and the K 2 O content reveal an evolved to highly evolved structure of the glauconite. It is abundant in shallow subtidal and tidal- and wave-influenced shoreface facies, and it forms either by the alteration of fecal pellets or as bioclast infillings. Micropaleontological and ichnological proxies confirm the glauconitization in an oxygen-depleted, shallow marine depositional setting. Small-sized (< 1 cm) fodinichnial, pascichnial, domichnial, and repichnial traces, and agglutinated foraminifera ( Bathysiphon) corroborate the oxygen-depleted seawater. The landward expansion of the oxygen minimum zone, leading to oxygen depletion in coastal settings, facilitates the glauconitization in a marginal marine setting. Kaolinite and Fe-Al-smectite react in the presence of excess Fe, forming Fe-rich Al-glauconite on the seafloor. Therefore, the compositionally unique glauconite reflects atypical seawater chemistry with increased elemental influx of Al, Fe, and Mg related to intense continental weathering, kaolinite substrate, and mobilization of Fe 2+ in oxygen-depleted seawater.

Landslides pose significant threats to ecosystems, lives, and economies, particularly in the geologically fragile Sub-Himalayan region of West Bengal, India. This study enhances landslide susceptibility prediction by developing an ensemble framework integrating Recursive Feature Elimination (RFE) with meta-learning techniques. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and a Meta Classifier (MC) were applied using Remote Sensing and GIS tools to identify key landslide-conditioning factors and classify susceptibility zones. Model performance was assessed through metrics such as accuracy, precision, recall, F1 score, and AUC of the ROC curve. Among the models, the Meta Classifier (MC) achieved the highest accuracy (0.956) and AUC (0.987), demonstrating superior predictive ability. Gradient Boosting (GB), XGBoost, and RF also performed well, with accuracies of 0.943 and AUC values of 0.987 (GB and XGBoost) and 0.983 (RF). Extremely Randomized Trees (ET) exhibited the highest accuracy (0.946) among individual models and an AUC of 0.985. SVM and LR, while slightly less accurate (0.941 and 0.860, respectively), provided valuable insights, with SVM achieving an AUC of 0.972 and LR achieving 0.935. The models effectively delineated landslide susceptibility into five zones (very low, low, moderate, high, and very high), with high and very high susceptibility zones concentrated in Darjeeling and Kalimpong subdivisions. These zones are influenced by intense rainfall, unstable geological structures, and anthropogenic activities like deforestation and urbanization. Notably, ET, RF, GB, and XGBoost demonstrated efficiency in feature selection, requiring fewer input variables while maintaining high performance. This study establishes a benchmark for landslide susceptibility mapping, providing a scalable and adaptable framework for geospatial hazard prediction. The findings hold significant implications for land-use planning, disaster management, and environmental conservation in vulnerable regions worldwide.

Although Paleogene warm climatic intervals have received considerable attention for atmospheric and oceanographic changes, the authigenic mineralization associated with these time spans remains overlooked. An extensive review of the literature reveals a close correspondence between the high abundance of glauconite and warm climatic intervals during the Paleogene period. The abundance of phosphorite, ironstone, lignite and black shale deposits reveals similar trends. Although investigated thoroughly, the origin of these authigenic deposits is never understood in the background of Paleogene warming climatic intervals. A combination of factors like warm seawater, hypoxic shelf, low rate of sedimentation, and enhanced rate of continental weathering facilitated the glauconitization. The last factor caused the excess supply of nutrients, including Fe, Si, K, Mg and Al through the rivers, the cations needed for the formation of glauconite. The excessive inflow of nutrient-rich freshwater into the shallow seas further ensured high organic productivity and stratification in shallow shelves, causing hypoxia. The consequent rapid rise in sea-level during the warm periods created extensive low-relief shallow marine shelves starved in sediments. Oxygen-deficiency in the shallow marine environment facilitated the fixation of Fe into the glauconite structure. The inflow of nutrient-rich water during the warm climatic intervals facilitated the formation of phosphorite, ironstone, and organic-matter-rich sedimentary deposits as well. Although global factors primarily controlled the formation of these authigenic deposits, local factors played significant roles in some of the deposits. Therefore, phosphorites formed in marine conditions with open circulation within the tropical zone. While lush growth of rainforest covers in the tropical belt facilitated the formation of coastal lignite.

Fibroblasts from patients with Tangier disease carrying ATP-binding cassette A1 (ABCA1) loss-of-function mutations are characterized by cardiolipin accumulation, a mitochondrial-specific phospholipid. Suppression of ABCA1 expression occurs in glomeruli from patients with diabetic kidney disease (DKD) and in human podocytes exposed to DKD sera collected prior to the development of DKD. We demonstrated that siRNA ABCA1 knockdown in podocytes led to reduced oxygen consumption capabilities associated with alterations in the oxidative phosphorylation (OXPHOS) complexes and with cardiolipin accumulation. Podocyte-specific deletion of Abca1 (Abca1fl/fl) rendered mice susceptible to DKD, and pharmacological induction of ABCA1 improved established DKD. This was not mediated by free cholesterol, as genetic deletion of sterol-o-acyltransferase-1 (SOAT1) in Abca1fl/fl mice was sufficient to cause free cholesterol accumulation but did not cause glomerular injury. Instead, cardiolipin mediates ABCA1-dependent susceptibility to podocyte injury, as inhibition of cardiolipin peroxidation with elamipretide improved DKD in vivo and prevented ABCA1-dependent podocyte injury in vitro and in vivo. Collectively, we describe a pathway definitively linking ABCA1 deficiency to cardiolipin-driven mitochondrial dysfunction. We demonstrated that this pathway is relevant to DKD and that ABCA1 inducers or inhibitors of cardiolipin peroxidation may each represent therapeutic strategies for the treatment of established DKD.

Background Autoimmune diseases have been associated with changes in the gut microbiome. In this study, the gut microbiome was evaluated in individuals with dry eye and bacterial compositions were correlated to dry eye (DE) measures. We prospectively included 13 individuals with who met full criteria for Sjögren’s (SDE) and 8 individuals with features of Sjögren’s but who did not meet full criteria (NDE) for a total of 21 cases as compared to 21 healthy controls. Stool was analyzed by 16S pyrosequencing, and associations between bacterial classes and DE symptoms and signs were examined. Results Results showed that Firmicutes was the dominant phylum in the gut, comprising 40–60% of all phyla. On a phyla level, subjects with DE (SDE and NDE) had depletion of Firmicutes (1.1-fold) and an expansion of Proteobacteria (3.0-fold), Actinobacteria (1.7-fold), and Bacteroidetes (1.3-fold) compared to controls. Shannon’s diversity index showed no differences between groups with respect to the numbers of different operational taxonomic units (OTUs) encountered (diversity) and the instances these unique OTUs were sampled (evenness). On the other hand, Faith’s phylogenetic diversity showed increased diversity in cases vs controls, which reached significance when comparing SDE and controls (13.57 ± 0.89 and 10.96 ± 0.76, p = 0.02). Using Principle Co-ordinate Analysis, qualitative differences in microbial composition were noted with differential clustering of cases and controls. Dimensionality reduction and clustering of complex microbial data further showed differences between the three groups, with regard to microbial composition, association and clustering. Finally, differences in certain classes of bacteria were associated with DE symptoms and signs. Conclusions In conclusion, individuals with DE had gut microbiome alterations as compared to healthy controls. Certain classes of bacteria were associated with DE measures.

Opiates are among the most prescribed drugs for pain management. However, morphine use or abuse results in significant gut bacterial translocation and predisposes patients to serious infections with gut origin. The mechanism underlying this defect is still unknown. In this report, we investigated the mechanisms underlying compromised gut immune function and bacterial translocation following morphine treatment. We demonstrate significant bacterial translocation to mesenteric lymph node (MLN) and liver following morphine treatment in wild-type (WT) animals that was dramatically and significantly attenuated in Toll-like receptor (TLR2 and 4) knockout mice. We further observed significant disruption of tight junction protein organization only in the ileum but not in the colon of morphine treated WT animals. Inhibition of myosin light chain kinase (MLCK) blocked the effects of both morphine and TLR ligands, suggesting the role of MLCK in tight junction modulation by TLR. This study conclusively demonstrates that morphine induced gut epithelial barrier dysfunction and subsequent bacteria translocation are mediated by TLR signaling and thus TLRs can be exploited as potential therapeutic targets for alleviating infections and even sepsis in morphine-using or abusing populations.