Browse Articles

Structural relaxation and domain formation in anisotropically strained La0.7Sr0.3MnO3/LaFeO3 superlattices on DyScO3(101)

Abstract Anisotropic strain engineering in epitaxial oxide films provides new opportunities to control the antiferromagnetic and structural properties crucial for advancements of antiferromagnetic spintronics. Here we report on a (La 0.7 Sr 0.3 MnO 3 /LaFeO 3 ) 4 superlattice grown on (101) o DyScO 3 substrate which imposes significant anisotropic in-plane strain. Reciprocal space mapping reveals selective strain relaxation along the tensile in-plane [010] o axis, while compression along the perpendicular in-plane [ $$\:\stackrel{-}{1}01$$ ] o axis remains strained. Scanning precession electron diffraction and higher-order Laue zone analysis show that the relaxation is accommodated by structural domain formation in the LaFeO 3 layers, initiating from the second bilayer and propagating out-of-plane. These domains minimise structural defects and correlate with the substrate step edges. X-ray magnetic dichroism measurements reveal bulk-like in-plane antiferromagnetic order with polydomain signature as previously reported. Our findings reveal the presence of structural domains coexisting with antiferromagnetic polydomain states, showing a strain-domain-magnetism relationship that provides insights for applications of strain engineering in spintronics applications.

Vorticity budget analysis of a mesoscale convective vortex during the July 2022 flash flood in Northern Tehran

Abstract Mesoscale Convective Vortices (MCVs) are influential mid-tropospheric systems capable of initiating or enhancing deep convection, yet their role in extreme precipitation over complex terrain remains insufficiently understood. This study examines the dynamical processes leading to the formation and intensification of an MCV associated with the 27 July 2022 heavy rainfall and deadly flash flood in northern Tehran. Using ERA5 data, GPM precipitation estimates, infrared satellite imagery, and Doppler radar observations, we document the multiscale evolution of the event and diagnose the vorticity budget throughout the troposphere. A coherent positive vorticity emerged south of the flood site in the late afternoon and migrated northward as convection intensified. Vorticity-budget diagnostics reveal that all four terms, horizontal advection, vertical advection, divergence (stretching), and tilting, made non-negligible contributions to the local vorticity tendency. Horizontal advection dominated the early development of the MCV, producing positive vorticity tendencies of order 10⁻⁸ s⁻² in the 700–600 hPa layer roughly three hours before rainfall onset. Divergence and tilting further amplified cyclonic vorticity below ~ 700 hPa, with tilting peaking near 850 hPa as strong vertical shear strengthened horizontal vorticity conversion. In contrast, vertical advection acted persistently as a compensating term, partially offsetting the low-level vorticity growth. The combined effect of these processes was a vertically coherent vortex column, aligned temporally with radar-observed convective organization. The extreme rainfall resulted from the interaction between a warm, humid monsoonal inflow from lower latitudes and a cooler air mass advected from higher latitudes, which enhanced low-level convergence, mesoscale ascent, and vortex stretching. This dynamically driven coupling between synoptic forcing, mesoscale vorticity generation, and complex topography produced an unexpectedly intense convective system. The findings highlight the importance of MCV-related vorticity processes in triggering high-impact precipitation events in mountainous mid-latitude regions and underscore the need for improved representation of these mechanisms in forecasting systems.

Intelligent high-throughput recognition model for bitter gourd fruit morphology and tubercle characteristics

Crystal structure of Echinococcus multilocularis cystatin B reveals a novel feature in classical stefins

Seawater-immersed glass-polyester composites with optimized mechanical properties

Cross-lingual sparse-MoE distillation for efficient low-resource assamese–english and bodo–english translation

Assessment of influencing factors of college and universities’ teaching effects using fuzzy and deep learning techniques

Social user geolocation based on K-medoids and Gaussian Kernel graph attention network

Phytochemical composition and antimicrobial efficacy of Nigerian polyherbal formulations against antibiotic-resistant micro-organisms

Testing the cross-cultural measurement invariance of the Penn state worry questionnaire for children (PSWQ-C) across Poland and the United States of America

Differences of anterior segment features in fellow eyes of primary angle closure glaucoma and healthy eyes

From data to decisions: the use of explainable AI to forecast soybean yield in major producing countries

Deep learning-based automatic segmentation and classification for cervical cancer detection using an improved U-Net and ensemble methods

Abstract Cervical cancer is the fourth leading cause of cancer-related illness and death among women worldwide. The Pap test is a widely used screening technique to detect abnormal cells that can become cancerous. In this research, we proposed a method for automatic segmentation and classification of cervical cancer cell images. The method uses an improved U-Net architecture to segment the image and identify the region of interest (ROI). Following segmentation, we classify the cervical cell type using ResNet50V2 and an ensemble of different pretrained models to enhance performance. We developed several pipelines for cervical cancer detection, including a normal method, with and without RES_DCGAN, before classification and segmentation tasks. The proposed method was evaluated using the Pomeranian, Herlev, and SIPaKMeD datasets. The experimental results showed that whole-cell segmentation achieved 99.53%, 88.95%, and 98.3% accuracy when RES_DCGAN was added before the segmentation. The framework achieved 96% and 95% accuracy for multi-class classification on the Pomeranian and SIPaKMeD datasets, respectively. Additionally, the Herlev dataset scored an accuracy of 91%, while SIPaKMeD achieved 99% for the binary classification of cervical cell types using the ensemble method. In conclusion, the deep learning-based segmentation and classification method demonstrated promising results for cervical cancer detection and can help pathologists diagnose the disease.

A machine learning-based short-term forecasting method for heavy fog in Anhui Province of China

Hemi-ellipsoid formula enables accurate assessment of carotid plaque volume and atherosclerotic burden

Significant efficiency of Ti-MOF and Ag-NPs in antiviral effect in PVY-tobacco pathosystem

Indirubin treatment was associated with modulation of the PI3K/AKT and MAPK pathways and induction of apoptosis and autophagy

Unveiling the anti-inflammatory activity of chloroform fraction of curcuma wallichii and its phytoconstituents by in vivo and in silico studies

Assessing cerebral capillary function and stalling using single capillary reporters in ultrasound localization microscopy

While transcranial neuroimaging of individual capillary function holds transformative potential for diagnostics, it has proven difficult to achieve. Superresolution ultrasound, while capable of achieving micron-scale resolution, relies on the accumulation of multiple microbubble events, a method inherently limited by the exceedingly low probability of observing such events within capillaries. We present single capillary reporters (SCaRe), a paradigm-shifting approach that utilizes the complete flow trajectory information extracted from individual microbubbles to directly image single capillaries. This method allows for transcranial reconstruction and functional assessment of deep capillary networks in the entire brain. We employed computational simulations and pathological neuroinflammation models to quantify and validate metrics such as capillary transit-time and capillary stalling. Importantly, we demonstrated SCaRe’s ability to resolve immune responses to injury at the single capillary level, markedly broadening research avenues for exploring microvascular dysfunction across diverse neurological conditions.

ACSS2 upregulation enhances neuronal resilience to aging and tau-associated neurodegeneration

Epigenetic mechanisms, including histone acetylation, regulate learning and memory and underlie Alzheimer’s disease and related dementia (ADRD). Acetyl-CoA synthetase 2 (ACSS2), an enzyme generating acetyl-CoA, locally regulates histone acetylation and gene expression in neuronal nuclei. This regulatory mechanism may be a promising target for therapeutic intervention in neurodegenerative diseases. Previously, we showed that systemic ACSS2 knockout mice, although largely normal in physiology, exhibit memory deficits. Here, we investigated whether increasing ACSS2 levels could protect neurons against disease and age-associated cognitive decline. Given the role of tau in ADRD, we used primary hippocampal neurons that mimic the sporadic development of tau pathology and the P301S transgenic mouse model for tau-induced memory decline. Our results show that ACSS2 upregulation mitigates tau-induced transcriptional alterations, enhances neuronal resilience against tau pathology, improves long-term potentiation, and ameliorates memory deficits. Additionally, boosting histone acetylation through ACSS2 countered age-related memory decline. These findings indicate that increasing ACSS2 is highly effective in countering age- and tau-induced transcriptome changes, preserving elevated levels of synaptic genes, and safeguarding synaptic integrity. These findings position ACSS2 as a key epigenetic regulator of cognitive aging and ADRD, highlighting its potential for targeted therapeutics to enhance brain resilience and function.