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In heritage sciences, the ability to obtain information about the origin and dating of cultural heritage objects is fundamental for placing an object into its historical context. Radiocarbon (14C) dating can help to identify the period during which a work of art was created by dating its constitutive materials. Such information can, however, only be obtained by removing a sample from the object, which is critical since art is irreplaceable and demands that the sampling be kept to a minimum. In this context, we propose a novel dating approach, which targets the natural organic binder of the pictorial layer as a new 14C candidate. In combination with spectroscopic techniques to ensure suitable sample selection, both canvas and paint samples were dated from three oil paintings. While not authenticating the paintings for belonging to a given artist, the 14C results from the baroque and neoclassical objects tend to align themselves with the purported attribution. The third object, attributed to the beginning of the 20th century’s modern expressionism movements, showcases the challenges in dating the natural organic binder owing to the presence of paraffi n wax. The presented case studies showcase, how 14C dating of the natural organic binder may complement or offer alternate routes of study in assessing an object’s historical context. Moreover, the importance of material studies in the sampling step is enlightened as a prerequisite to access reliable 14C ages., Dans les sciences du patrimoine, la capacité d’obtenir des informations sur l’origine et la datation des objets du patrimoine culturel est fondamentale pour les replacer dans leur contexte historique. La datation par le radiocarbone (carbone 14, noté 14 C) peut aider à identifier la période pendant laquelle une œuvre d’art a été créée en datant ses matériaux constitutifs. Une telle information ne peut cependant être obtenue qu’en prélevant un échantillon de l’objet, ce qui est critique car une œuvre d’art est unique et exige que l’échantillonnage soit réduit au minimum. Dans ce contexte, nous proposons une nouvelle approche de datation qui cible le liant organique naturel de la couche picturale. Étayés par les données d’analyses spectroscopiques, permettant une sélection d’échantillons appropriée, des prélèvements de toile et de matière picturale ont été datés sur trois peintures à l’huile. Même si les résultats de datation ne permettent pas d’attribuer les peintures à un artiste donné, ils peuvent cependant confirmer une période de création. Le troisième objet, lié aux débuts des divers mouvements expressionnistes modernes du XX e siècle, met en lumière les enjeux de datation du liant organique naturel du fait de la présence de cire de paraffine. Les études de cas présentées montrent comment la datation par le 14 C du liant organique naturel peut compléter ou offrir d’autres voies d’étude pour évaluer le contexte historique d’un objet. Les études de matériaux lors de l’échantillonnage sont également un prérequis indispensable pour accéder à des âges radiocarbone fiables.

Motivation: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively., Bioinformatics, 36 (S2), ISSN:1367-4803, ISSN:1460-2059

Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availability https://github.com/ratschlab/scim, bioRxiv

Surface-functionalized hollow carbon nanobubbles containing platinum in their interior perform size-selective catalysis., Selective dealloying of metal nanoparticles results in rattle-type hollow carbon nanoshells enclosing platinum nanoparticles, which are able to perform size-selective catalysis. Selective functionalization of the outer graphene-like carbon surface prevents agglomeration and leads to well dispersible nanocatalysts in aqueous solutions. The synthesis starts with the production of nanoparticles with a cobalt–platinum-alloy core surrounded by graphene-like carbon via reducing flame spray synthesis. After surface functionalization, simultaneous pore formation in the shell-wall and dissolution of the cobalt results in platinum encapsulated in hollow carbon nanospheres. Catalytic oxidation of differently sized sugars (glucose and maltoheptaose) reveales size-selective catalytic properties of these platinum nanorattles.