ગાંધીયુગીન ગુજરાતી કવિતામાં સત્યાગ્રહ અને અહિંસાનું નિરૂપણ

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Homo sapiens in Arabia by 85,000 years ago

Understanding the timing and character of Homo sapiens expansion out of Africa is critical for inferring the colonisation and admixture processes that underpin global population history. It has been argued that dispersal out of Africa had an early phase, particularly ~130-90 thousand years ago (ka), that only reached the East Mediterranean Levant, and a later phase, ~60-50 ka, that extended across the diverse environments of Eurasia to Sahul. However, recent findings from East Asia and Sahul challenge this model. Here we show that H. sapiens was in the Arabian Peninsula before 85 ka. We describe the Al Wusta-1 (AW-1) intermediate phalanx from the site of Al Wusta in the Nefud Desert, Saudi Arabia. AW-1 is the oldest directly dated fossil of our species outside Africa and the Levant. The palaeoenvironmental context of Al Wusta demonstrates that H. sapiens using Middle Palaeolithic stone tools dispersed into Arabia during a phase of increased precipitation driven by orbital forcing, in association with a primarily African fauna. A Bayesian model incorporating independent chronometric age estimates indicates a chronology for Al Wusta of ~95-86 ka, which we correlate with a humid episode in the later part of Marine Isotope Stage 5 known from various regional records. Al Wusta shows that early dispersals were more spatially and temporally extensive than previously thought. Early H. sapiens dispersals out of Africa were not limited to winter rainfall-fed Levantine Mediterranean woodlands immediately adjacent to Africa, but extended deep into the semi-arid grasslands of Arabia, facilitated by periods of enhanced monsoonal rainfall

Apelin Attenuates the Osteoblastic Differentiation of Vascular Smooth Muscle Cells

Vascular calcification, which results from a process osteoblastic differentiation of vascular smooth muscle cells (VSMCs), is a major risk factor for cardiovascular morbidity and mortality. Apelin is a recently discovered peptide that is the endogenous ligand for the orphan G-protein-coupled receptor, APJ. Several studies have identified the protective effects of apelin on the cardiovascular system. However, the effects and mechanisms of apelin on the osteoblastic differentiation of VSMCs have not been elucidated. Using a culture of calcifying vascular smooth muscle cells (CVMSCs) as a model for the study of vascular calcification, the relationship between apelin and the osteoblastic differentiation of VSMCs and the signal pathway involved were investigated. Alkaline phosphatase (ALP) activity and osteocalcin secretion were examined in CVSMCs. The involved signal pathway was studied using the extracellular signal-regulated kinase (ERK) inhibitor, PD98059, the phosphatidylinositol 3-kinase (PI3-K) inhibitor, LY294002, and APJ siRNA. The results showed that apelin inhibited ALP activity, osteocalcin secretion, and the formation of mineralized nodules. APJ protein was detected in CVSMCs, and apelin activated ERK and AKT (a downstream effector of PI3-K). Suppression of APJ with siRNA abolished the apelin-induced activation of ERK and Akt. Furthermore, inhibition of APJ expression, and the activation of ERK or PI3-K, reversed the effects of apelin on ALP activity. These results showed that apelin inhibited the osteoblastic differentiation of CVSMCs through the APJ/ERK and APJ/PI3-K/AKT signaling pathway. Apelin appears to play a protective role against arterial calcification

Distribution of 137Cs in benthic plants along depth profiles in the outer Puck Bay (Baltic Sea)

A study was conducted on three macroalgae species: Polysiphonia fucoides and Furcellaria lumbricalis, the species of the red algae division, and Cladophora glomerata, representing the green algae division, as well as Zostera marina, representing vascular plants. The main aim of the study was to recognize the level of (137)Cs concentrations in the plants, which could be used as a measurement of bioaccumulation efficiency in the selected macrophytes at varying depths, and in the seasonal resolution of the vegetation period: spring–summer and autumnal. The plants’ biomass clearly showed seasonal variability, as did the (137)Cs concentrations in the plants. Cesium activity also changed with depth. Seasonal variability in radionuclide content in the plants, as well as the differences in its activity determined along the depth profile, were related mainly to the plant biomass and the dilution effect caused by the biomass increment and reflected the growth dynamics. P. fucoides showed much greater bioaccumulation ability at each depth as compared to C. glomerata, a green algae. Lower concentrations of (137)Cs were also identified in F. lumbricalis and in Z. marina, mostly as a result of differences in morphology and physiology. P. fucoides can be recommended as a bioindicator for the monitoring of (137)Cs contamination due to the high efficiency of bioaccumulation and the available biomass along the depth profile, as well as the occurrence throughout the entire vegetation season

Contextual Classification of Polarimetric Sar Data Through a Complex-Valued Kernel and Global Energy Minimization

This paper addresses the challenges of supervised semantic segmentation using Polarimetric Synthetic Aperture Radar (PolSAR) data for land cover mapping. We extend previous approaches relying on spatial-contextual classifier based on Support Vector Machines (SVMs) and Markov Random Field (MRF) models. The kernel used in this work extends a previously presented complex formulation based on reproducing kernel Hilbert spaces (RKHS). In this paper, we present a symmetrized form of this complex kernel, integrating it with global energy minimization techniques, and show that it provides more accurate predictions. The proposed approach achieves competitive accuracy on benchmark datasets, comparable to those of deep learning algorithms. The method's advantage lies in its lower resource requirements, making it a promising alternative for PolSAR semantic segmentation

Heterogeneous change detection with PRISMA and COSMO-SkyMed Second Generation imagery for natural disaster management

Change detection (CD) is among the most important tools in natural disaster monitoring. Special emphasis is on heterogeneous CD methods, which allow for a faster response. In this paper, we propose a novel heterogeneous CD method tailored at working with image domains of very different dimensionality, which allows for a greater applicational flexibility. The proposed method integrates deep image-to-image translation, spectral clustering concepts, and manifold learning, and works in a fully unsupervised manner, further enforcing a fast implementation in real-world scenarios. From an application-oriented perspective, the focus is on the recent PRISMA and COSMO-SkyMed missions of the Italian Space Agency

Manifold learning and deep generative networks for heterogeneous change detection from hyperspectral and synthetic aperture radar images

Unsupervised change detection stands as a critical tool for damage assessment after a natural disaster. We emphasize heterogeneous change detection methods, which support the case of highly heterogeneous images at the two observation dates, providing greater flexibility than traditional homogeneous methods. This adaptability is vital for swift responses in the aftermath of natural disasters. In this framework, we address the challenging case of detecting changes between a hyperspectral and a synthetic aperture radar images. This case has intrinsic difficulties, namely the difference in the nature of the physical quantity measured, added to the great difference in dimensionality of the two imaging domains. To address these challenges, a novel method is proposed based on the integration of a manifold learning technique and deep learning networks trained to perform an image to image translation task. The method works in a fully unsupervised manner, further enforcing a fast implementation in real-world scenarios. From an application-oriented perspective, we focus on flooded-area mapping using the PRISMA and COSMO-SkyMed missions. The experimental validation on two datasets, a semi-simulated one and a real one associated with flooding, suggests that the proposed method allows for accurate detection of flooded areas and other ground changes