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Currently, the Morpho-Constitutional Analysis (MCA) is the de facto approach for the etiological diagnosis of kidney stone formation, and it is an important step for establishing personalized treatment to avoid relapses. More recently, research has focused on performing such tasks intra-operatively, an approach known as Endoscopic Stone Recognition (ESR). Both methods rely on features observed in the surface and the section of kidney stones to separate the analyzed samples into several sub-groups. However, given the high intra-observer variability and the complex operating conditions found in ESR, there is a lot of interest in using AI for computer-aided diagnosis. However, current AI models require large datasets to attain a good performance and for generalizing to unseen distributions. This is a major problem as large labeled datasets are very difficult to acquire, and some classes of kidney stones are very rare. Thus, in this paper, we present a method based on diffusion as a way of augmenting pre-existing ex-vivo kidney stone datasets. Our aim is to create plausible diverse kidney stone images that can be used for pre-training models using ex-vivo data. We show that by mixing natural and synthetic images of CCD images, it is possible to train models capable of performing very well on unseen intra-operative data. Our results show that is possible to attain an improvement of 10% in terms of accuracy compared to a baseline model pre-trained only on ImageNet. Moreover, our results show an improvement of 6% for surface images and 10% for section images compared to a model train on CCD images only, which demonstrates the effectiveness of using synthetic images., Comment: 8 pages, 6 figures, 1 table, conference paper

Non--Contact Atomic Force Microscopy with CO--functionalized metal tips (referred to as HR-AFM) provides access to the internal structure of individual molecules adsorbed on a surface with totally unprecedented resolution. Previous works have shown that deep learning (DL) models can retrieve the chemical and structural information encoded in a 3D stack of constant-height HR--AFM images, leading to molecular identification. In this work, we overcome their limitations by using a well-established description of the molecular structure in terms of topological fingerprints, the 1024--bit Extended Connectivity Chemical Fingerprints of radius 2 (ECFP4), that were developed for substructure and similarity searching. ECFPs provide local structural information of the molecule, each bit correlating with a particular substructure within the molecule. Our DL model is able to extract this optimized structural descriptor from the 3D HR--AFM stacks and use it, through virtual screening, to identify molecules from their predicted ECFP4 with a retrieval accuracy on theoretical images of 95.4\%. Furthermore, this approach, unlike previous DL models, assigns a confidence score, the Tanimoto similarity, to each of the candidate molecules, thus providing information on the reliability of the identification. By construction, the number of times a certain substructure is present in the molecule is lost during the hashing process, necessary to make them useful for machine learning applications. We show that it is possible to complement the fingerprint-based virtual screening with global information provided by another DL model that predicts from the same HR--AFM stacks the chemical formula, boosting the identification accuracy up to a 97.6\%. Finally, we perform a limited test with experimental images, obtaining promising results towards the application of this pipeline under real conditions

Optimization is a critical tool for addressing a broad range of human and technical problems. However, the paradox of advanced optimization techniques is that they have maximum utility for problems in which the relationship between the structure of the problem and the ultimate solution is the most obscure. The existence of solution with limited insight contrasts with techniques that have been developed for a broad range of engineering problems where integral transform techniques yield solutions and insight in tandem. Here, we present a ``Pareto-Laplace'' integral transform framework that can be applied to problems typically studied via optimization. We show that the framework admits related geometric, statistical, and physical representations that provide new forms of insight into relationships between objectives and outcomes. We argue that some known approaches are special cases of this framework, and point to a broad range of problems for further application., Comment: 28+19 pages, 3 figures

Atomic Force Microscopy (AFM) operating in the frequency modulation mode with a metal tip functionalized with a CO molecule images the internal structure of molecules with an unprecedented resolution. The interpretation of these images is often difficult, making the support of theoretical simulations important. Current simulation methods, particularly the most accurate ones, require expertise and resources to perform ab initio calculations for the necessary inputs (i.e charge density and electrostatic potential of the molecule). Here, we propose an efficient and simple alternative to simulate these AFM images based on a Conditional Generative Adversarial Network (CGAN), that avoids all force calculations, and uses as the only input a 2D ball--and--stick depiction of the molecule. We discuss the performance of the model when optimized using different training subsets. Our CGAN reproduces accurately the intramolecular contrast observed in the simulated images for quasi--planar molecules, but has significant limitations for molecules with a significant internal torsion, due to the strictly 2D character of the input.

The functioning of double-stranded (ds) nucleic acids (NAs) in cellular processes is strongly mediated by their elastic response. These processes involve proteins that interact with dsDNA or dsRNA and distort their structures. The perturbation of the elasticity of NAs arising from these deformations is not properly considered by most theoretical frameworks. In this work, we introduce a novel method to assess the impact of mechanical stress on the elastic response of dsDNA and dsRNA through the analysis of the fluctuations of the double helix. Application of this approach to atomistic simulations reveals qualitative differences in the force dependence of the mechanical properties of dsDNA with respect to those of dsRNA, which we relate to structural features of these molecules by means of physically-sound minimalistic models., Comment: 6 pages, 2 figures

Despite being the main tool to visualize molecules at the atomic scale, AFM with CO-functionalized metal tips is unable to chemically identify the observed molecules. Here we present a strategy to address this challenging task using deep learning techniques. Instead of identifying a finite number of molecules following a traditional classification approach, we define the molecular identification as an image captioning problem. We design an architecture, composed of two multimodal recurrent neural networks, capable of identifying the structure and composition of an unknown molecule using a 3D-AFM image stack as input. The neural network is trained to provide the name of each molecule according to the IUPAC nomenclature rules. To train and test this algorithm we use the novel QUAM-AFM dataset, which contains almost 700,000 molecules and 165 million AFM images. The accuracy of the predictions is remarkable, achieving a high score quantified by the cumulative BLEU 4-gram, a common metric in language recognition studies., Comment: 30 pages, 4 figures, 2 tables, includes supplementary information (with additional 21 pages, 9 figures, 1 table)

Frequency modulation (FM) Atomic Force Microscopy (AFM) with metal tips functionalized with a CO molecule at the tip apex has provided access to the internal structure of molecules with totally unprecedented resolution. We propose a model to extract the chemical information from those AFM images in order to achieve a complete identification of the imaged molecule. Our Conditional Generative Adversarial Network (CGAN) converts a stack of AFM images at various tip-sample distances into a ball-and-stick depiction, where balls of different color and size represent the chemical species and sticks represent the bonds, providing complete information on the structure and chemical composition. The CGAN has been trained and tested with the QUAM-AFM data set, that contains simulated AFM images for a collection of 686,000 molecules that include all the chemical species relevant in organic chemistry. Tests with a large set of theoretical images and few experimental examples demonstrate the accuracy and potential of our approach for molecular identification., Comment: 32 pages, 5 figures, includes supplementary information (with additional 11 pages, 3 figures, 1 table)

V393 Scorpii is a member of the subclass of Algols dubbed Double Periodic Variables (DPVs). These are semidetached binaries with B-type primaries showing a long-photometric cycle lasting in average 33 times the orbital period. We describe the behavior of unreported metallic emission lines in the cool stellar component of this system. The emissions can be single or double for a same line and sometimes show velocity shifts regarding the velocity of the center of mass of the star. In addition, these lines are stronger during the high state. This behavior suggests the presence of active regions in the surface of the rapidly rotating A7 donor covering a fraction of the visible hemisphere, which have larger emissivity during the high state. Our finding supports the recently proposed dynamo model for the long cycle of DPVs proposed by Schleicher & Mennickent. The model predicts an increase of the dynamo number of the donor during epochs of mass transfer in this system, and a theoretical long/orbital period ratio very close to the observed one at the present system age., Comment: 1 table, 8 figures. To be published in journal Publication of the Astronomical Society of the Pacific

Over many years, computational simulations based on Density Functional Theory (DFT) have been used extensively to study many different materials at the atomic scale. However, its application is restricted by system size, leaving a number of interesting systems without a high-accuracy quantum description. In this work, we calculate the electronic and structural properties of a graphene-metal system significantly larger than in previous plane-wave calculations with the same accuracy. For this task we use a localised basis set with the \textsc{Conquest} code, both in their primitive, pseudo-atomic orbital form, and using a recent multi-site approach. This multi-site scheme allows us to maintain accuracy while saving computational time and memory requirements, even in our exemplar complex system of graphene grown on Rh(111) with and without intercalated atomic oxygen. This system offers a rich scenario that will serve as a benchmark, demonstrating that highly accurate simulations in cells with over 3000 atoms are feasible with modest computational resources., Comment: 11 pages, 3 figures, submitted to J. Chem. Theor. Comput

Single molecule force spectroscopy of DNA strands adsorbed at surfaces is a powerful technique used in air or liquid environments to quantify their mechanical properties. Although the force responses are limited to unfolding events so far, single base detection might be possible in more drastic cleanliness conditions such as ultra high vacuum. Here, we report on high resolution imaging and pulling attempts at low temperature (5K) of a single strand DNA (ssDNA) molecules composed of 20 cytosine bases adsorbed on Au(111) by scanning probe microscopy and numerical calculations. Using electrospray deposition technique, the ssDNA were successfully transferred from solution onto a surface kept in ultra high vacuum. Real space characterizations reveal that the ssDNA have an amorphous structure on gold in agreement with numerical calculations. Subsequent substrate annealing promotes the desorption of solvent molecules, DNA as individual molecules as well as the formation of DNA self assemblies. Furthermore, pulling experiments by force spectroscopy have been conducted to measure the mechanical response of the ssDNA while detaching. A periodic pattern of 0.2 to 0.3nm is observed in the force curve which arises from the stick slip of single nucleotide bases over the gold. Although an intra molecular response is obtained in the force curve, a clear distinction of each nucleotide detachment is not possible due the complex structure of ssDNA adsorbed on gold.

We present a theoretical study of the electronic transport through Pt nanocontacts. We show that the analysis of the tunnelling regime requires a very careful treatment of the technical details. For instance, an insufficient size of the system can cause unphysical charge oscillations to arise along the transport direction; moreover, the use of an inappropriate basis set can deviate the distance dependence of the conductance from the expected exponential trend. While the conductance decay can be either corrected by employing ghost atoms or a large-cutoff-radius basis set, the same does not apply to the corrugation, for which only the second option is recommended. Interestingly, these details were not found to have a remarkable impact in the contact regime. These findings are important for theoretical studies of distance-dependent phenomena in scanning-probe and break-junction experiments., Comment: 7 pages, 8 figures

Contrary to most materials, graphene exhibits a negative thermal expansion coefficient (TEC), i.e it contracts when heated. This contraction is due to the thermal excitation of low energy out-of-plane vibration modes. These flexural modes have been reported to govern the electronic transport and the mechanical response of suspended graphene. In this work, we systematically investigate the influence of defects in the TEC of suspended graphene membranes. Controlled introduction of low densities of mono-vacancies reduces the graphene TEC, up to one order of magnitude for a defect density of $5\times10^{12}$~cm$^{-2}$ . Our molecular dynamics simulations reproduce the observed trend and show that TEC reduction is due to the suppression of out-of-plane fluctuations caused by the strain fields created by mono-vacancies in their surrounding areas. These results highlight the key role of defects in the properties of "real-life" graphene, and pave the way for future proposals of electronic and mechanical defect engineering., Comment: 19