The activation of CO2 on defective surface of Cu(I)/TiO2–x has been studied using in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). It was demonstrated that CO2 – species, generated upon an electron attachment to CO2, are spontaneously dissociated into CO even in the dark on a partially oxygen depleted Cu(I)/TiO2–x surface prepared by thermal annealing in an inert environment. The formation of CO bound on Cu+ sites was identified in the DRIFT spectra, and isotopic carbon-labeling experiments confirmed that the produced CO was derived from CO2. The spontaneous dissociation of CO2 – in the dark is to a large extent associated with the surface oxygen vacancies that provide not only the electronic charge (i.e., formation of Ti3+) but also the sites for the adsorption of oxygen atoms from CO2. The surface Cu+ species may facilitate destabilizing adsorbed CO2 – and enhance its subsequent dissociation to CO. The defective surface is much more active than defect-free surface; the healed oxygen vacancies after CO2 reduction can be easily regenerated via inert gas annealing at a moderate temperature (i.e., 300 °C). Compared with in the dark, CO2 activation and dissociation under photoillumination is remarkably improved, possibly because of sustained electron supply and partial regeneration of surface oxygen vacancy induced by irradiation. The results from DRIFTS analysis were verified by the measurement of catalytic activity using gas chromatography. These findings are important to advancing the understanding in the chemistry of CO2 adsorption and photocatalytic reduction on the surface of metal oxide catalysts.

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MotivationThe evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers’ attention.MethodsIn this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).ResultsThis method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some “dark genes” that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective.

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W e give a fast and simple factor 2.74 approximation algorithm for the problem of choosing the k medians of the continuum of demand points defined by a convex polygon C. Our algorithm first surrounds the input region with a bounding box, then subdivides the bounding box into subregions with equal area. Simulation results on the convex hulls of the 50 states in the United States show that the practical performance of our algorithm is within 10% of the optimal solution in the vast majority of cases.

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