Synthesis and Reduction of Iron(III) Porphinone Complexes and Their Spectroscopy Studies

The vibrational spectra of iron(I) porphinone, and related species were studied in this work. The iron(I) complexes were synthesized by the sodium anthracenide reduction method. The extent of reduction was monitored by UV-visible spectroscopy. The products were precipitated with heptane. Efforts to obtain single crystals of the iron(I) complex were unsuccessful, but procedures for further work were developed. The deuteration of the methylene protons was studied. These macrocycles of these complexes can be used for further studies by vibrational spectroscopy. The infrared and resonance Raman spectra of iron(I) porphinone in KBr were obtained and interpreted. Further studies using deuterated macrocycles and DFT calculations can be used to better understand the electronic structures of the formal iron(I) state

Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which suffer from two drawbacks. First, they have to make a trade-off between the expressiveness of higher-order cross features and the computational cost, resulting in suboptimal predictions. Second, enumerating all the cross features, including irrelevant ones, may introduce noisy feature combinations that degrade model performance. In this work, we propose the Adaptive Factorization Network (AFN), a new model that learns arbitrary-order cross features adaptively from data. The core of AFN is a logarithmic transformation layer to convert the power of each feature in a feature combination into the coefficient to be learned. The experimental results on four real datasets demonstrate the superior predictive performance of AFN against the start-of-the-arts.Comment: Accepted by AAAI'2

Key pathways and genes controlling the development and progression of clear cell renal cell carcinoma (ccRCC) based on gene set enrichment analysis

BACKGROUND: Clear-cell renal cell carcinoma (ccRCC) is one of the most common types of kidney cancer in adults; however, its causes are not completely understood. The study was designed to filter the key pathways and genes associated with the occurrence or development of ccRCC, acquaint its pathogenesis at gene and pathway level, to provide more theory evidence and targeted therapy for ccRCC. METHODS: Gene set enrichment analysis (GSEA) and meta-analysis (Meta) were used to screen the critical pathways and genes which may affect the occurrence and progression of ccRCC on the transcription level. Corresponding pathways of significant genes were obtained with the online website DAVID (http://david.abcc.ncifcrf.gov/). RESULTS: Thirty seven consistent pathways and key genes in these pathways related to ccRCC were obtained with combined GSEA and meta-analysis. These pathways were mainly involved in metabolism, organismal systems, cellular processes and environmental information processing. CONCLUSION: The gene pathways that we identified could provide insight concerning the development of ccRCC. Further studies are needed to determine the biological function for the positive genes