RESUMEN
A classic challenge in chemical sensing is selectivity. Metal-organic frameworks (MOFs) are an exciting class of materials because they can be tuned for selective chemical adsorption. Adsorption events trigger work-function shifts, which can be detected with a chemical-sensitive field-effect transistor (power ≈microwatts). In this work, several case studies were used towards generalizing the sensing mechanism, ultimately towards our metal-centric hypothesis. HKUST-1 was used as a proof-of-principle humidity sensor. The response is thickness independent, meaning the response is surface localized. ZIF-8 is demonstrated to be an NO2 -sensing material, and the response is dominated by adsorption at metal sites. Finally, MFM-300(In) shows how standard hard-soft acid-base theory can be used to qualitatively predict sensor responses. This paper sets the groundwork for using the tunability of metal-organic frameworks for chemical sensing with distributed, scalable devices.
RESUMEN
Reactions that change the identity of an atom within a ring system are emerging as valuable tools for the site-selective editing of molecular structures. Herein, we describe the expansion of an underdeveloped transformation that directly converts azaarene-derived N-oxides to all-carbon arenes. This ring transmutation exhibits good functional group tolerance and replaces the N-oxide moiety with either unsubstituted, substituted, or isotopically labeled carbon atoms in a single laboratory operation.
RESUMEN
Identifying cancer risk groups by multi-omics has attracted researchers in their quest to find biomarkers from diverse risk-related omics. Stratifying the patients into cancer risk groups using genomics is essential for clinicians for pre-prevention treatment to improve the survival time for patients and identify the appropriate therapy strategies. This study proposes a multi-omics framework that can extract the features from various omics simultaneously. The framework employs autoencoders to learn the non-linear representation of the data and applies tensor analysis for feature learning. Further, the clustering method is used to stratify the patients into multiple cancer risk groups. Several omics were included in the experiments, namely methylation, somatic copy-number variation (SCNV), micro RNA (miRNA) and RNA sequencing (RNAseq) from two cancer types, including Glioma and Breast Invasive Carcinoma from the TCGA dataset. The results of this study are promising, as evidenced by the survival analysis and classification models, which outperformed the state-of-the-art. The patients can be significantly (p-value<0.05) divided into risk groups using extracted latent variables from the fused multi-omics data. The pipeline is open source to help researchers and clinicians identify the patients' risk groups using genomics.
Asunto(s)
Variaciones en el Número de Copia de ADN , Genómica , Humanos , Genómica/métodos , Metilación de ADN , Neoplasias/genética , MicroARNs/genética , Femenino , Biomarcadores de Tumor/genética , Glioma/genética , Glioma/patología , Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , MultiómicaRESUMEN
The selective functionalization of strong, typically inert carbon-hydrogen (C-H) bonds in organic molecules is changing synthetic chemistry. However, the undirected functionalization of primary C-H bonds without competing functionalization of secondary C-H bonds is rare. The borylation of alkyl C-H bonds has occurred previously with this selectivity, but slow rates required the substrate to be the solvent or in large excess. We report an iridium catalyst ligated by 2-methylphenanthroline with activity that enables, with the substrate as limiting reagent, undirected borylation of primary C-H bonds and, when primary C-H bonds are absent or blocked, borylation of strong secondary C-H bonds. Reactions at the resulting carbon-boron bond show how these borylations can lead to the installation of a wide range of carbon-carbon and carbon-heteroatom bonds at previously inaccessible positions of organic molecules.