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The objective of the study was to investigate the expression and functions of CASC9 in esophageal squamous cell carcinoma (ESCC). Long noncoding RNAs (lncRNAs) upregulated in ESCC tissues were detected by RNA sequencing. Expression of CASC9 was determined from clinical samples and cell lines by qRT‐PCR. The effects of CASC9 knockdown on migration and invasion were evaluated by wound healing assay, cell migration and invasion assays in vitro. We found that the lncRNA, CASC9, was markedly upregulated in ESCC tissues. Furthermore, knockdown of CASC9 significantly suppressed cell migration and invasion in vitro. Furthermore, enhanced CASC9 expression level was correlated with differentiation. The results indicated that CASC9 is significantly upregulated in ESCC tissues and may represent a new marker of poor prognosis and a potential therapeutic target for esophageal cancer intervention.

Metal organic frameworks (MOFs) are actively being explored as potential adsorbed natural gas storage materials for small vehicles. Experimental exploration of potential materials is limited by the throughput of synthetic chemistry. We here describe a computational methodology to complement and guide these experimental efforts. The method uses known chemical transformations in silico to identify MOFs with high methane deliverable capacity. The procedure explicitly considers synthesizability with geometric requirements on organic linkers. We efficiently search the composition and conformation space of organic linkers for nine MOF networks, finding 48 materials with higher predicted deliverable capacity (at 65 bar storage, 5.8 bar depletion, and 298 K) than MOF-5 in four of the nine networks. The best material has a predicted deliverable capacity 8% higher than that of MOF-5.

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Metal-organic frameworks (MOFs) offer unprecedented atom-scale design and structural tunability, largely due to the vast number of possible organic linkers which can be utilized in their assembly. Exploration of this space of linkers allows identification of ranges of achievable material properties as well as discovery of optimal materials for a given application. Experimental exploration of the linker space has to date been quite limited due to the cost and complexity of synthesis, while high-throughput computational studies have mainly explored MOF materials based on known or readily available linkers. Here an evolutionary algorithm for de novo design of organic linkers for metal-organic frameworks is used to predict MOFs with either high methane deliverable capacity or methane accessible surface area. Known chemical reactions are applied in silico to a population of linkers to discover these MOFs. Through this design strategy, MOF candidates are found in the ten symmetric networks acs, cds, dia, hxg, lvt, nbo, pcu, rhr, sod, and tbo. The correlation between deliverable capacities and surface area is network dependent.