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We present spatially resolved VLT/SINFONI spectroscopy with adaptive optics of type-2 active galactic nuclei (AGN) from the SINFONI Survey for Unveiling the Physics and Effect of Radiative feedback (SUPER), which targeted X-ray bright (L2 − 10 keV ≳ 1042 erg s−1) AGN at cosmic noon (z ∼ 2). Our analysis of the rest-frame optical spectra unveils ionised outflows in all seven examined targets, as traced via [O III]λ5007 line emission, moving at v ≳ 600 km s−1. These outflows are clearly spatially resolved in six objects and extend on 2–4 kpc scales, but they are marginally resolved in the remaining one object. Interestingly, these SUPER type-2 AGN are all heavily obscured sources (NH ≳ 1023 cm−2) and host faster ionised outflows than their type-1 counterparts within the same range of bolometric luminosity (Lbol ∼ 1044.8 − 46.5 erg s−1). SUPER has hence provided observational evidence that the dichotomy of type-1 to type-2 at z ∼ 2 might not be driven simply by projection effects, but might reflect two distinct obscuring life stages of active galaxies, as predicted by evolutionary models. Within this picture, SUPER type-2 AGN might be undergoing the blow-out phase, where the large amount of obscuring material efficiently accelerates large-scale outflows via radiation pressure on dust, eventually unveiling the central active nucleus and signaling the start of the bright, unobscured type-1 AGN phase. Moreover, the velocities of the overall population of ionised outflows detected in SUPER are comparable with the escape speed of their dark matter haloes, and they are in general high enough to reach distances of 30–50 kpc from the centre. These outflows are hence likely to sweep away the gas (at least) out of the baryonic disk and/or to heat the host gas reservoir, thus reducing and possibly quenching star formation. [ABSTRACT FROM AUTHOR]

Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning. Hyperspectral (HS) imaging (HSI) is an emerging multidimensional optical imaging method that captures detailed spectral information across multiple wavelengths, allowing for the identification of nuanced differences in tissue composition, with the potential to enhance intraoperative tissue classification. However, current frameworks often require retraining models for each HSI to extract meaningful features, resulting in long processing times and high computational costs. Additionally, most methods utilise the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To overcome these challenges, we propose a novel approach called MedDiffHSI, which combines diffusion and transformer techniques. Our method involves training an unsupervised learning framework based on the diffusion model to extract high-level and low-level spectral-spatial features from HSI. This approach eliminates the need for retraining of spectral-spatial feature learning model, thereby reducing time complexity. We then extract intermediate multistage features from different timestamps for classification using a pretrained denoising U-Net. To fully explore and exploit the rich contextual semantics and textual information hidden in the extracted diffusion feature, we utilise a spectral-spatial attention module. This module not only learns multistage information about features at different depths, but also extracts and enhances effective information from them. Finally, we employ a supervised transformer-based classifier with weighted majority voting (WMV) to perform the HSI classification. To validate our approach, we conduct comprehensive experiments on in vivo brain database data sets and also extend the analysis to include additional HSI data sets for breast cancer to evaluate the framework performance across different types of tissue. The results demonstrate that our framework outperforms existing approaches by using minimal training samples (5%) while achieving state-of-the-art performance., (© 2024 The Author(s). Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.)

Hyperspectral imaging has gained popularity for analysing remotely sensed images in various fields such as agriculture and medical. However, existing models face challenges in dealing with the complex relationships and characteristics of spectral-spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a novel approach called DiffSpectralNet, which combines diffusion and transformer techniques. The diffusion method is able extract diverse and meaningful spectral-spatial features, leading to improvement in HSI classification. Our approach involves training an unsupervised learning framework based on the diffusion model to extract high-level and low-level spectral-spatial features, followed by the extraction of intermediate hierarchical features from different timestamps for classification using a pre-trained denoising U-Net. Finally, we employ a supervised transformer-based classifier to perform the HSI classification. We conduct comprehensive experiments on three publicly available datasets to validate our approach. The results demonstrate that our framework significantly outperforms existing approaches, achieving state-of-the-art performance. The stability and reliability of our approach are demonstrated across various classes in all datasets., (© 2024. Crown.)

Regeneration of injured tendons and ligaments (T/L) is a worldwide need. In this study electrospun hierarchical scaffolds made of a poly-L (lactic) acid/collagen blend were developed reproducing all the multiscale levels of aggregation of these tissues. Scanning electron microscopy, microCT and tensile mechanical tests were carried out, including a multiscale digital volume correlation analysis to measure the full-field strain distribution of electrospun structures. The principal strains (ε p1 and ε p3 ) described the pattern of strains caused by the nanofibers rearrangement, while the deviatoric strains (ε D ) revealed the related internal sliding of nanofibers and bundles. The results of this study confirmed the biomimicry of such electrospun hierarchical scaffolds, paving the way to further tissue engineering and clinical applications., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alberto Sensini has patent HIERARCHICAL MULTISCALE ELECTROSPUN SCAFFOLD FOR THE REGENERATION AND/OR REPLACEMENT OF THE TENDINOUS/LIGAMENTOUS TISSUE AND A METHOD FOR ITS PRODUCTION issued to Alma Mater Studiorum - Università di Bologna. Andrea Zucchelli has patent HIERARCHICAL MULTISCALE ELECTROSPUN SCAFFOLD FOR THE REGENERATION AND/OR REPLACEMENT OF THE TENDINOUS/LIGAMENTOUS TISSUE AND A METHOD FOR ITS PRODUCTION issued to Alma Mater Studiorum - Università di Bologna. Luca Cristofolini has patent HIERARCHICAL MULTISCALE ELECTROSPUN SCAFFOLD FOR THE REGENERATION AND/OR REPLACEMENT OF THE TENDINOUS/LIGAMENTOUS TISSUE AND A METHOD FOR ITS PRODUCTION issued to Alma Mater Studiorum - Università di Bologna. Maria Letizia Focarete has patent HIERARCHICAL MULTISCALE ELECTROSPUN SCAFFOLD FOR THE REGENERATION AND/OR REPLACEMENT OF THE TENDINOUS/LIGAMENTOUS TISSUE AND A METHOD FOR ITS PRODUCTION issued to Alma Mater Studiorum - Università di Bologna. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Autograft or metal implants are routinely used in skeletal repair. However, they fail to provide long-term clinical resolution, necessitating a functional biomimetic tissue engineering alternative. The use of native human bone tissue for synthesizing a biomimetic material ink for three-dimensional (3D) bioprinting of skeletal tissue is an attractive strategy for tissue regeneration. Thus, human bone extracellular matrix (bone-ECM) offers an exciting potential for the development of an appropriate microenvironment for human bone marrow stromal cells (HBMSCs) to proliferate and differentiate along the osteogenic lineage. In this study, we engineered a novel material ink (LAB) by blending human bone-ECM (B) with nanoclay (L, Laponite ® ) and alginate (A) polymers using extrusion-based deposition. The inclusion of the nanofiller and polymeric material increased the rheology, printability, and drug retention properties and, critically, the preservation of HBMSCs viability upon printing. The composite of human bone-ECM-based 3D constructs containing vascular endothelial growth factor (VEGF) enhanced vascularization after implantation in an ex vivo chick chorioallantoic membrane (CAM) model. The inclusion of bone morphogenetic protein-2 (BMP-2) with the HBMSCs further enhanced vascularization and mineralization after only seven days. This study demonstrates the synergistic combination of nanoclay with biomimetic materials (alginate and bone-ECM) to support the formation of osteogenic tissue both in vitro and ex vivo and offers a promising novel 3D bioprinting approach to personalized skeletal tissue repair., Supplementary Information: The online version contains supplementary material available at 10.1007/s42242-023-00265-z., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s) 2024.)