Organocatalytic Asymmetric Construction and Application of Axially Chiral Spiro-bisindoles.

Spiro skeletons have emerged as a privileged class of chiral carriers across various research fields, including asymmetric catalysis and functional materials, due to their remarkable configurational rigidity. However, limited structural diversity of spiro frameworks significantly restricts the expansion of their applications. Here we present a new class of axially chiral spiro-bisindole frameworks and report their first enantioselective construction via a chiral phosphoric acid-catalyzed intramolecular dehydrative cyclization reaction.

A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study.

Background: Clinical appearance and high-frequency ultrasound (HFUS) are indispensable for diagnosing skin diseases by providing internal and external information. However, their complex combination brings challenges for primary care physicians and dermatologists. Thus, we developed a deep multimodal fusion network (DMFN) model combining analysis of clinical close-up and HFUS images for binary and multiclass classification in skin diseases.

The urban morphology on our planet - Global perspectives from space.

Urbanization is the second largest mega-trend right after climate change. Accurate measurements of urban morphological and demographic figures are at the core of many international endeavors to address issues of urbanization, such as the United Nations' call for "Sustainable Cities and Communities". In many countries - particularly developing countries -, however, this database does not yet exist.

Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model.

As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model.