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Correction to: Growth factor-triggered de-sialylation controls glycolipid-lectin-driven endocytosis (Nature Cell Biology, (2025), 27, 3, (449-463), 10.1038/s41556-025-01616-x)
No full textCorrection to: Nature Cell Biologyhttps://doi.org/10.1038/s41556-025-01616-x, published online 21 February 2025. In the version of the article initially published, the Agence Nationale pour la Recherche grant no. ANR-23-CE13-0033-01 was missing from the Acknowledgements section and has now been added to the HTML and PDF versions of the article
In Vitro and In-Silico Assessment of Gaussian Curvature-driven Internalization Kinetics of Nanoparticles
No full textNanoparticles have been of significant interest in various biomedical domains such as drug delivery, gene delivery, cytotoxicity analysis, and imaging. Despite the synthesis of a variety of nanoparticles, their cellular uptake efficiency remains a substantial obstacle, with only a small fraction of delivered nanoparticles (NPs) have been reported to traverse the cell membrane within 24 h. Consequently, higher doses are often necessitated, leading to increased toxicity concerns. In this investigation, we illustrate that nanoparticles having negative Gaussian curvature demonstrate rapid and efficient internalization into cells by lowering the energy barrier for membrane bending. Specifically, three types of gold nanoparticles; gold nanorods (GNR), gold nanodogbones at pH 4 (GDB4), and gold nanodogbones at pH 6 (GDB6) were synthesized, with Gaussian curvatures of 0, −166.91, and −376.62, respectively. Cellular uptake studies conducted via ICP-OES analysis reveal that GDB6 is taken up 140% more in A549 cells and 77% more in NIH3T3 cells compared to GNR. Confocal microscopy-based uptake studies further confirm the higher uptake of GDB6 compared to GNR. Additionally, molecular simulations indicate that GDB nanoparticles exhibit a significantly larger free energy change during translocation compared to GNR, emphasizing the impact of nanoparticle shape on uptake and translocation through the membrane and validating the efficacy of negative Gaussian curvature in enhancing cellular uptake, consistent with experimental observations. Overall, our findings emphasize the importance of nanoparticle curvature modulation in maximizing cellular uptake efficiency for improved biomedical applications, providing valuable insights into the design of nanomaterials for drug delivery purposes
Advanced Luminescent Material for Multikey Static and Dynamic Anticounterfeiting and Information Encryption
No full textCurrent static luminescent anticounterfeiting techniques exhibit limited security efficacy, highlighting an urgent demand for more advanced anticounterfeiting technologies. In this study, we present a persistent luminescent material, Zn2.95Ga2SnO8:Cr3+/Ho3+/Yb3+, which additionally demonstrates upconversion (UC) emission capabilities. The multifaceted emission characteristics of this material were utilized to create a high-concealment information encryption-decryption label. Our investigation indicates that the persistent luminescence (PersL) is attributable to the presence of suitably positioned traps within the phosphor matrix. Importantly, the phosphor also exhibits near-infrared (NIR) excited PersL, resulting from energy transfer processes between Ho3+and Cr3+ions. We successfully showcased the potential for dynamic anticounterfeiting and dual-mode information encryption-decryption by integrating this versatile material into anticounterfeiting patterns. The capacity to excite these phosphors using cost-effective UVA flashlights, combined with the visibility of their emissions to the naked eye and standard smartphone cameras, underscores their viability for large-scale applications in anticounterfeiting and secure information technologies
Insights into Indian summer monsoon rainfall variability: early twentieth century warming vs. mid-twentieth century cooling
No full textThe north–south asymmetry in Indian Summer Monsoon Rainfall (ISMR) anomalies of the 1930s provides critical insights into the effects of Early Twentieth Century Warming (ETCW) on regional climate dynamics. In ETWC, central and northeast India experienced a significant increase in rainfall, while south peninsular India recorded a notable decline. After the 1930s, this trend reversed during the Mid-20th Century Cooling (MTCC), with decreasing rainfall trend in central and northeast India and increasing rainfall trend in the south peninsular India, which were significant at the 0.05 level. The heightened rainfall in central and northeast India during the ETCW is attributed to more frequent monsoon depressions in the Bay of Bengal (BoB), driven by increased low-level vorticity and reduced vertical wind shear. The contrasting temperatures between the warmer land and cooler ocean intensified the low-level jet and moisture transport over the northern Arabian Sea, increasing wind speed and moisture transport into the BoB. Additionally, positive phases of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation suppressed upper-level winds over the BoB, further reducing vertical wind shear and promoting monsoon depression formation. During the transition to the MTCC, land cooling and Indian Ocean warming reduced the land-sea thermal contrast, resulting in decreased rainfall in central and northeast India and increased rainfall in south peninsular India. The dipole-like ISMR trend is linked to global teleconnection through the internal variability of ISMR, particularly the variation of synoptic-scale systems
A study on removal of Enoxacin, an emerging pharmaceutical pollutant from wastewater using graphene oxide via batch and continuous processing methods
No full textMedFocusCLIP : improving few shot classification in medical datasets using pixel wise attention
No full textWith the popularity of foundational models, parameter efficient fine tuning has become the defacto approach to leverage pretrained models to perform downstream tasks. Taking inspiration from recent advances in large language models, Visual Prompt Tuning, and similar techniques, learn an additional prompt to efficiently finetune a pretrained vision foundational model. However, we observe that such prompting is insufficient for fine-grained visual classification tasks such as medical image classification, where there is large inter-class variance, and small intra-class variance. Hence, in this paper we propose to leverage advanced segmentation capabilities of Segment Anything Model 2 (SAM2) as a visual prompting cue to help visual encoder in the CLIP (Contrastive Language-Image Pretraining) by guiding the attention in CLIP visual encoder to relevant regions in the image. This helps the model to focus on highly discriminative regions, without getting distracted from visually similar background features, an essential requirement in a fewshot, finegrained classification setting. We evaluate our method on diverse medical datasets including X-rays, CT scans, and MRI images, and report an accuracy of (71%, 81%, 86%, 58%) from the proposed approach on (COVID, lung-disease, brain-tumor, breast-cancer) datasets against (66%, 70%, 68%, 29%) from a pretrained CLIP model after fewshot training. The proposed approach also allows to obtain interpretable explanation for the classification performance through the localization obtained using segmentation
A New Transform for Robust Text-Independent Speaker Identification
No full textThis paper proposes a new method of feature extraction for robust text-independent speaker identification. The focus of this work is on applications which yield higher identification accuracy without increasing the computational effort. The impetus for this new feature extraction technique comes from a new transformation. We have proposed this transform from speaker identification perspective. A complete experimental evaluation was conducted on a database of 61 speakers with Gaussian mixture speaker model. This new feature extraction technique has been compared with mel-frequency cepstral coefficient (MFCC) feature. Evaluation results show, that the new feature provides better identification accuracy than the MFCC feature. The discrimination capability of the feature sets have been evaluated statistically, using F-ratio and J-measure. Experimental results show that the new feature set is much more discriminative than the MFCC feature set
A conceptual framework to assess the dynamics of human-non-human-marine areas in India during anthropocene: A case study of Odisha
No full textOceans play a vital role in sustaining marine life and human well-being, offering essential ecosystem services, including regulating coastal temperature and rainfall, supporting ecosystems such as the coral reef ecosystem, mangrove ecosystem, and estuarine ecosystem, food production, carbon sequestration, and tourism among others. Numerous marine species depend on healthy ocean ecosystems and their survival is directly influenced by the scale of anthropogenic activities in the region. It is important to study the interconnectedness of humans and non-humans in marine areas to fully appreciate their mutual dependencies and implications for ocean health and sustainability.
Earlier studies have explored human-centric approaches for sustainable management of marine resources, which are beneficial for both people and marine ecosystems. These studies suggest that human-centered frameworks could also enhance the protection of non-humans in the oceans. However, there is a lack of research on this topic specific to India, given its extensive 7,500 km coastline. This underscores the need to investigate the human–non-human–marine area nexus in this part of the world, to support the environmentally sustainable management of its marine regions.
The study addresses this gap by identifying, acknowledging and documenting the contributions of marine non-humans to humans, and how they support the region’s economy, ecology, and communities. It attempts to investigate key indicators playing a critical role in human-marine environment dynamics. While the study emphasizes large marine animals in the human-marine environmental dynamics, it also acknowledges the significance of other marine species, habitats, and ecosystems, including phytoplankton and zooplankton. The study includes systematic literature review considering possible sources of information and stakeholder consultations at various levels.
The study would result in a guiding framework, highlighting the importance of establishing institutional structures addressing the human–non-human–marine area nexus. At its nascent stage, it offers strategic recommendations aimed at safeguarding human rights in coastal areas while simultaneously protecting and conserving marine biodiversity. This framework can serve as a valuable reference for policymakers and researchers who are seeking effective measures and inclusive approaches for better marine resource management and marine ecosystem governance
Fourier Domain Gradient Descent Total Least Square/Fourth Algorithm for Efficient Adaptive Direction of Arrival Estimation
No full textDirection-of-arrival (DOA) estimation is formulated within an adaptive-filtering framework that partitions the sensor array into a reference element and an auxiliary array. The auxiliary-array signal is filtered and subtracted from the reference to produce an error, minimized by the complex least-mean-square (LMS) algorithm. Although LMS converges rapidly with a large step size, it exhibits degraded steady-state performance; conversely, the complex least-mean-fourth (LMF) algorithm yields better steady-state accuracy but slower convergence. To combine their strengths, we propose two algorithms: complex LMS/F, which adaptively switches between LMS and LMF algorithms according to a threshold parameter; and complex GD-TLS/F, which employs a gradient-descent total-least-squares criterion to enhance robustness against noisy inputs. We derive the cost functions and weight update rules for both algorithms and introduce a novel computationally efficient Fourier domain approach for DOA estimation from the adaptive filter weights. A comprehensive theoretical analysis that includes a global optimal solution, mean stability, steady-state mean-square performance, and mean-square convergence is presented. Extensive simulation results demonstrate that the proposed algorithms achieve lower estimation error compared to existing adaptive algorithms