Silk reinforced with graphene or carbon nanotubes spun by spiders

Here, we report the production of silk incorporating graphene and carbon nanotubes directly by spider spinning, after spraying spiders with the corresponding aqueous dispersions. We observe a significant increment of the mechanical properties with respect to the pristine silk, in terms of fracture strength, Young's and toughness moduli. We measure a fracture strength up to 5.4 GPa, a Young's modulus up to 47.8 GPa and a toughness modulus up to 2.1 GPa, or 1567 J/g, which, to the best of our knowledge, is the highest reported to date, even when compared to the current toughest knotted fibres. This approach could be extended to other animals and plants and could lead to a new class of bionic materials for ultimate applications

Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language

We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts. The recently proposed RNR-Map employs a grid structure comprising latent codes positioned at each pixel. These latent codes, which are derived from image observation, enable: i) image rendering given a camera pose, since they are converted to Neural Radiance Field; ii) image navigation and localization with astonishing accuracy. On top of this, we enhance RNR-Map with CLIP-based embedding latent codes, allowing natural language search without additional label data. We evaluate the effectiveness of this map in single and multi-object searches. We also investigate its compatibility with a Large Language Model as an "affordance query resolver". Code and videos are available at https://intelligolabs.github.io/Le-RNR-Map/Comment: Accepted at ICCVW23 VLA

The role of low-energy electrons in the charging process of LISA test masses

The estimate of the total electron yield is fundamental for our understanding of the test-mass charging associated with cosmic rays in the Laser Interferometer Space Antenna (LISA) Pathfinder mission and in the forthcoming gravitational wave observatory LISA. To unveil the role of low energy electrons in this process owing to galactic and solar energetic particle events, in this work we study the interaction of keV and sub-keV electrons with a gold slab using a mixed Monte Carlo (MC) and ab-initio framework. We determine the energy spectrum of the electrons emerging from such a gold slab hit by a primary electron beam by considering the relevant energy loss mechanisms as well as the elastic scattering events. We also show that our results are consistent with experimental data and MC simulations carried out with the GEANT4-DNA toolkit

THz-assisted microscopy of silica matrix for biological materials encapsulation: a theoretical and experimental study

In this study, we use THz-assisted atom probe tomography (APT) to analyse silica matrices used to encapsulate biomolecules. This technique provides the chemical composition and 3D structure without significantly heating the biosample, which is crucial for studying soft organic molecules such as proteins. Our results show that THz pulses and a positive static field trigger controlled evaporation of silica matrices, enabling 4D imaging with chemical sensitivity comparable to UV laser-assisted APT. To support the interpretation of these experimental results, we devise a computational model based on time-dependent density functional theory to describe the interaction between silica matrices and THz radiation. This model captures the nonlinear dynamics driven by THz-pulses and the interplay between the THz source and the static electric field in real time. This interdisciplinary approach expands the capabilities of APT and holds promise for other THz-based analyses offering new insights into material dynamics in complex biological environments

Designing Logic Tensor Networks for Visual Sudoku puzzle classification

Given the increasing importance of the neurosymbolic (NeSy) approach in artificial intelligence, there is a growing interest in studying benchmarks specifically designed to emphasize the ability of AI systems to combine low-level representation learning with high-level symbolic reasoning. One such recent benchmark is Visual Sudoku Puzzle Classification, that combines visual perception with relational constraints. In this work, we investigate the application of Logic Tensork Networks (LTNs) to the Visual Sudoku Classification task and discuss various alternatives in terms of logical constraint formulation, integration with the perceptual module and training procedure

Prevalence of HPV Infection in Racial-Ethnic Subgroups of Head and Neck Cancer Patients

The landscape of HPV infection in racial/ethnic subgroups of head and neck cancer (HNC) patients has not been evaluated carefully. In this study, a meta-analysis examined the prevalence of HPV in HNC patients of African ancestry. Additionally, a pooled analysis of subject-level data was also performed to investigate HPV prevalence and patterns of p16 (CDNK2A) expression amongst different racial groups. Eighteen publications (N = 798 Black HNC patients) were examined in the meta-analysis, and the pooled analysis included 29 datasets comprised of 3,129 HNC patients of diverse racial/ethnic background. The meta-analysis revealed that the prevalence of HPV16 was higher among Blacks with oropharyngeal cancer than Blacks with non-oropharyngeal cancer. However, there was great heterogeneity observed among studies (Q test P<0.0001). In the pooled analysis, after adjusting for each study, year of diagnosis, age, gender and smoking status, the prevalence of HPV16/18 in oropharyngeal cancer patients was highest in Whites (61.1%), followed by 58.0% in Blacks and 25.2% in Asians (P<0.0001). There was no statistically significant difference in HPV16/18 prevalence in non-oropharyngeal cancer by race (P=0.682). With regard to the pattern of HPV16/18 status and p16 expression, White patients had the highest proportion of HPV16/18+/p16+ oropharyngeal cancer (52.3%), while Asians and Blacks had significantly lower proportions (23.0% and 22.6%, respectively) [P <0.0001]. Our findings suggest that the pattern of HPV16/18 status and p16 expression in oropharyngeal cancer appears to differ by race and this may contribute to survival disparities

Prevalence of HPV infection in racial–ethnic subgroups of head and neck cancer patients

The landscape of human papillomavirus (HPV) infection in racial/ethnic subgroups of head and neck cancer (HNC) patients has not been evaluated carefully. In this study, a meta-analysis examined the prevalence of HPV in HNC patients of African ancestry. Additionally, a pooled analysis of subject-level data was also performed to investigate HPV prevalence and patterns of p16 (CDNK2A) expression amongst different racial groups. Eighteen publications (N=798 Black HNC patients) were examined in the meta-analysis, and the pooled analysis included 29 datasets comprised of 3129 HNC patients of diverse racial/ethnic background. The meta-analysis revealed that the prevalence of HPV16 was higher among Blacks with oropharyngeal cancer than Blacks with non-oropharyngeal cancer. However, there was great heterogeneity observed among studies (Q test P<0.0001). In the pooled analysis, after adjusting for each study, year of diagnosis, age, gender and smoking status, the prevalence of HPV16,18 in oropharyngeal cancer patients was highest in Whites (61.1%), followed by 58.0% in Blacks and 25.2% in Asians (P<0.0001). There was no statistically significant difference in HPV16,18 prevalence in non-oropharyngeal cancer by race (P=0.682). With regard to the pattern of HPV16,18 status and p16 expression, White patients had the highest proportion of HPV16,18+/p16+ oropharyngeal cancer (52.3%), while Asians and Blacks had significantly lower proportions (23.0 and 22.6%, respectively) [P<0.0001]. Our findings suggest that the pattern of HPV16,18 status and p16 expression in oropharyngeal cancer appears to differ by race and this may contribute to survival disparities

Global wealth disparities drive adherence to COVID-safe pathways in head and neck cancer surgery

Peer reviewe

Elettrocarbossilazione di bromobenzeni su catodi di argento

L’attivazione elettrochimica del legame carbonio-alogeno è un ambito ampiamente sfruttato in elettrochimica organica. La reazione è di grande interesse, poiché ha un ruolo importante in ambito sintetico e ambientale. Nell’ambito dell’interesse per i processi di elettrocarbossilazione di alogenuri organici, recentemente è stata dedicata una rilevante attenzione alla carbossilazione di composti aromatici, in particolare benzeni sostituiti, per la produzione di intermedi di chimica fine. I buoni risultati ottenuti per l’elettrocarbossilazione del bromobenzene su Ag, hanno suggerito la necessità di approfondire il meccanismo di tale processo e di estenderlo a bromobenzeni sostituiti. Innanzi sono stati indagati il comportamento voltammetrico del bromobenzene su diversi materiali catodici (Ag, Au, Cu, Pt, Pd, Ni, Zn, Fe, acciaio), per verificarne le potenzialità elettrocatalitiche ed individuare i materiali che hanno migliori capacità elettrocatalitiche, mediante il confronto con il comportamento su un materiale inerte come il glassy carbon (GC). Il migliori materiali catodici trovati sono l’argento, il rame e l’oro. Alla luce di questi risultati si è condotta l’indagine voltammetrica dei composti in esame su catodi di Ag. Una volta ottenute le informazioni dalle indagini voltammetriche, si sono condotti i processi di elettrocarbossilazione su diversi substrati per verificare la possibilità di realizzare carbossilazioni elettrocatalitiche su Ag. I prodotti dell’ elettrocarbossilazione sono stati analizzati mediante tecnica HPLC. I risultati mostrano rese buone, mediamente superiori al 50%, ma inferiori alle aspettative; inoltre è necessaria una più approfondita analisi circa le reazioni parassite che sfavoriscono il processo di elettrocarbossilazione

Diffusion-Based Image Generation for In-Distribution Data Augmentation in Surface Defect Detection

In this study, we show that diffusion models can be used in industrial scenarios to improve the data augmentation procedure in the context of surface defect detection. In general, defect detection classifiers are trained on ground-truth data formed by normal samples (negative data) and samples with defects (positive data), where the latter are consistently fewer than normal samples. For these reasons, state-of-the-art data augmentation procedures add synthetic defect data by superimposing artifacts to normal samples. This leads to out-of-distribution augmented data so that the classification system learns what is not a normal sample but does not know what a defect really is. We show that diffusion models overcome this situation, providing more realistic in-distribution defects so that the model can learn the defect’s genuine appearance. We propose a novel approach for data augmentation that mixes out-of-distribution with in-distribution samples, which we call In&Out. The approach can deal with two data augmentation setups: i) when no defects are available (zero-shot data augmentation) and ii) when defects are available, which can be in a small number (few-shot) or a large one (full-shot). We focus the experimental part on the most challenging benchmark in the state-of-the-art, i.e., the Kolektor Surface-Defect Dataset 2, defining the new state-of-the-art classification AP score under weak supervision of .782. The code is available at https://github.com/intelligolabs/in_and_out