Metal ion interactions with DNA have far-reaching implications in biochemistry and DNA nanotechnology. Ag⁺ is uniquely interesting because it binds exclusively to the bases rather than the backbone of DNA, without the toxicity of Hg²⁺. In contrast to prior studies of Ag⁺ incorporation into double-stranded DNA, we remove the constraints of Watson-Crick pairing by focusing on homo-base DNA oligomers of the canonical bases. High resolution electro-spray ionization mass spectrometry reveals an unanticipated Ag⁺-mediated pairing of guanine homo-base strands, with higher stability than canonical guanine-cytosine pairing. By exploring unrestricted binding geometries, quantum chemical calculations find that Ag⁺ bridges between non-canonical sites on guanine bases. Circular dichroism spectroscopy shows that the Ag⁺-mediated structuring of guanine homobase strands persists to at least 90 °C under conditions for which canonical guanine-cytosine duplexes melt below 20 °C. These findings are promising for DNA nanotechnology and metal-ion based biomedical science.

We present an efficient scheme to calculate the chiroptical response of molecular systems within time dependent density functional theory using either a real-time propagation or a frequency-dependent Sternheimer method. The scheme avoids the commonly used sum over empty orbitals and has a very favorable scaling with system size. Moreover, the method is general and can be easily implemented. In the present work, we implemented it using a real-space pseudo-potential representation of the wave-functions and Hamiltonian. The specific use of non-local pseudo-potentials implies that a gauge correction term in the angular momentum operator must be included to ensure that the total scheme is fully gauge invariant. Applications to small organic chiral molecules are shown and discussed, addressing some deficiencies of present exchange–correlation functionals to describe the absolute position of the excitations. However, the shape or sign of the dichroism spectra comes out in excellent agreement with available experiments.

DNA oligomers can form silver-mediated duplexes, stable in gas phase and solution, with potential for novel biomedical and technological applications. The nucleobase-metal bond primarily drives duplex formation, but hydrogen (H−) bonds may also be important for structure selection and stability. To elucidate the role of H-bonding, we conducted theoretical and experimental studies of a duplex formed by silver-mediated cytosine homopobase DNA strands, two bases long. This silver-mediated cytosine tetramer is small enough to permit accurate, realistic modeling by DFT-based quantum mechanics/molecular mechanics methods. In gas phase, our calculations found two energetically favorable configurations distinguished by H-bonding, one with a novel interplane H-bond, and the other with planar H-bonding of silver-bridged bases. Adding solvent favored silver-mediated tetramers with interplane H-bonding. Overall agreement of electronic circular dichroism spectra for the final calculated structure and experiment validates these findings. Our results can guide use of these stabilization mechanisms for devising novel metal-mediated DNA structures.

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision and natural language processing (YOLO and TF-IDF, respectively). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

The paper’s main contributions are twofold: to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare; and to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia (Italy) since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection, uses socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.