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A novel formal cross-coupling of aryl methyl sulfones and alcohols affording alkyl aryl ethers via an SRN1 pathway is developed. Two marketed antitubercular drugs were efficiently prepared employing this approach as the key step. A dimsyl-anion initiated radical chain process was revealed as the major pathway. DFT calculations indicate that the formation of a radical anion via nucleophilic addition of alkoxide to the aryl radical is the key step in determining the observed chemoselectivity.

Cephalotaxine-type alkaloids (CTAs), represented by homoharringtonine (HHT, 1), display potent efficacy against different types of leukemia cells. In this study, a method for hydrogenation of β-substituted itaconic acid monoesters with chiral Ru[DTBM-SegPhos](OAc)

A novel formal cross-coupling of aryl methyl sulfones and alcohols affording alkyl aryl ethers via an S

Additional file 1. Algorithm of parameters in Table 1 and raw data

In this study, estimates of the growth rate of new infections, based on the growth rate of new laboratory-confirmed cases, were used to provide a statistical basis for in-depth research into the epidemiological patterns of H7N9 epidemics. The incubation period, interval from onset to laboratory confirmation, and confirmation time for all laboratory-confirmed cases of H7N9 avian influenza in Mainland China, occurring between January 2013 and June 2017, were used as the statistical data. Stochastic processes theory and maximum likelihood were used to calculate the growth rate of new infections. Time-series analysis was then performed to assess correlations between the time series of new infections and new laboratory-confirmed cases. The rate of new infections showed significant seasonal fluctuation. Laboratory confirmation was delayed by a period of time longer than that of the infection (average delay, 13 days; standard deviation, 6.8 days). At the lags of −7.5 and −15 days, respectively, the time-series of new infections and new confirmed cases were significantly correlated; the cross correlation coefficients (CCFs) were 0.61 and 0.16, respectively. The temporal distribution characteristics of new infections and new laboratory-confirmed cases were similar and strongly correlated.

Background Outbreaks of respiratory infectious diseases often take place in crowded places. To understand the spreading pattern of an outbreak of a respiratory infectious disease and provide a theoretical basis for the targeted implementation of scientific prevention and control, we attempted to establish a stochastic model to simulate an outbreak of a respiratory infectious disease at a military camp. This model fits the general pattern of disease transmission and further enriches theories on the transmission dynamics of infectious diseases. Methods We established an enclosed system of 500 people exposed to adenovirus type 7 in a military camp. During the infection period, the patients transmitted the virus randomly to susceptible people. The spread of the epidemic under militarized management mode was simulated using a computer model named “the random collision model”, and the effects of factors such as the basic reproductive number ( R 0 ), time of isolation of the patients (TOI), interval between the onset and isolation (IOI), and immunization rates (IR) on the developmental trend of the epidemic were quantitatively analysed. Results Once the R 0 exceeds 1.5, the median attack rate increases sharply; when R 0 =3, with a delay in the TOI, the attack rate increases gradually and eventually remains stable. If the IOI exceeds 2.3 days, the median attack rate will also increase dramatically. If the IR exceeds 0.5, the median of the attack rate nears zero. The median generation time was 8.26 days (95% CI: 7.84-8.69 days). The partial rank correlation coefficients between the attack rate of the epidemic and the R 0 , TOI, IOI, and IR were 0.61, 0.17, 0.45, and -0.27, respectively. Conclusion The random collision model not only simulates how an epidemic spreads with superior precision but also allows more flexibility in the settings of the exposure population’s activities and different types of infectious diseases, which is conducive to furthering the exploration of the epidemiological characteristics of epidemic outbreaks.