No abstract

Question Answering (QA), the process of computing valid answers to questions formulated in natural language, has recently gained attention in both industry and academia. Translating this idea to the realm of geographic information systems (GIS) may open new opportunities for data scientists. In theory, analysts may simply ask spatial questions to exploit diverse geographic information resources, without a need to know how GIS tools and geodata sets interoperate. In this outlook article, we investigate the scientific challenges of geo-analytical question answering, introducing the problems of unknown answers and indirect QA. Furthermore, we argue why core concepts of spatial information play an important role in addressing this challenge, enabling us to describe analytic potentials, and to compose spatial questions and workflows for generating answers. ARTICLE HISTORY

Diabetic wound is one of the most intractable chronic wounds that is prone to bacterial infection. Hypoxia is an important feature in its microenvironment. However, it is challenging for antimicrobial therapy to directly apply the existing hypoxia-responsive drug delivery systems due to the active targeting deficiency and the biofilm obstacle. Herein, we customizes a hypoxia-responsive carrier, lactose-modified azocalix[4]arene (LacAC4A) with the ability to actively target and inhibit biofilm. By loading ciprofloxacin (Cip), the resultant supramolecular nanoformulation Cip@LacAC4A demonstrates enhanced antibacterial efficacy resulting from both the increased drug accumulation and the controlled release at the site of infection. When applied on diabetic wounds together with multidrug-resistant Pseudomonas aeruginosa infection in vivo, Cip@LacAC4A induces definitely less inflammatory infiltration than free Cip, which translates into high wound healing performance. Importantly, such design principle provides a direction for developing antimicrobial drug delivery systems.

N 6-methyladenosine (m 6 A) is the most abundant post-transcriptional modification in mRNA, and regulates critical biological functions via m 6 A reader proteins that bind to m 6 A-containing transcripts. There exist multiple m 6 A reader proteins in the human genome, but their respective binding specificity and functional relevance under different biological contexts are not yet fully understood due to the limitation of experimental approaches. An in silico study was devised to unveil the target specificity and regulatory functions of different m 6 A readers. We established a support vector machine-based computational framework to predict the epitranscriptome-wide targets of six m 6 A reader proteins (YTHDF1-3, YTHDC1-2, and EIF3A) based on 58 genomic features as well as the conventional sequence-derived features. Our model achieved an average AUC of 0.981 and 0.893 under the full-transcript and mature mRNA model, respectively, marking a substantial improvement in accuracy compared to the sequence encoding schemes tested. Additionally, the distinct biological characteristics of each individual m 6 A reader were explored via the distribution, conservation, Gene Ontology enrichment, cellular components and molecular functions of their target m 6 A sites. A web server was constructed for predicting the putative binding readers of m 6 A sites to serve the research community, and is freely accessible at: http://m6areader.rnamd.com.