London : European Collaborating Centres in Addiction Studies, 1998.

Scott H. Holan, Robert Lund, Ginger Davis

Source: Statist. Surv., Volume 4, 232--274.

Abstract:
Autoregressive moving-average (ARMA) difference equations are ubiquitous models for short memory time series and have parsimoniously described many stationary series. Variants of ARMA models have been proposed to describe more exotic series features such as long memory autocovariances, periodic autocovariances, and count support set structures. This review paper enumerates, compares, and contrasts the common variants of ARMA models in today’s literature. After the basic properties of ARMA models are reviewed, we tour ARMA variants that describe seasonal features, long memory behavior, multivariate series, changing variances (stochastic volatility) and integer counts. A list of ARMA variant acronyms is provided.

References:
Aknouche, A. and Guerbyenne, H. (2006). Recursive estimation of GARCH models. Communications in Statistics-Simulation and Computation 35 925–938.

Alzaid, A. A. and Al-Osh, M. (1990). An integer-valued pth-order autoregressive structure (INAR (p)) process. Journal of Applied Probability 27 314–324.

Anderson, P. L., Tesfaye, Y. G. and Meerschaert, M. M. (2007). Fourier-PARMA models and their application to river flows. Journal of Hydrologic Engineering 12 462–472.

Ansley, C. F. (1979). An algorithm for the exact likelihood of a mixed autoregressive-moving average process. Biometrika 66 59–65.

Basawa, I. V. and Lund, R. (2001). Large sample properties of parameter estimates for periodic ARMA models. Journal of Time Series Analysis 22 651–663.

Bauwens, L., Laurent, S. and Rombouts, J. V. K. (2006). Multivariate GARCH models: A survey. Journal of Applied Econometrics 21 79–109.

Bertelli, S. and Caporin, M. (2002). A note on calculating autocovariances of long-memory processes. Journal of Time Series Analysis 23 503–508.

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 307–327.

Bollerslev, T. (2008). Glossary to ARCH (GARCH). CREATES Research Paper 2008-49.

Bollerslev, T., Engle, R. F. and Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances. The Journal of Political Economy 96 116–131.

Bondon, P. and Palma, W. (2007). A class of antipersistent processes. Journal of Time Series Analysis 28 261–273.

Bougerol, P. and Picard, N. (1992). Strict stationarity of generalized autoregressive processes. The Annals of Probability 20 1714–1730.

Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (2008). Time Series Analysis: Forecasting and Control, 4th ed. Wiley, New Jersey.

Breidt, F. J., Davis, R. A. and Trindade, A. A. (2001). Least absolute deviation estimation for all-pass time series models. Annals of Statistics 29 919–946.

Brockwell, P. J. (1994). On continuous-time threshold ARMA processes. Journal of Statistical Planning and Inference 39 291–303.

Brockwell, P. J. (2001). Continuous-time ARMA processes. In Stochastic Processes: Theory and Methods, ( D. N. Shanbhag and C. R. Rao, eds.). Handbook of Statistics 19 249–276. Elsevier.

Brockwell, P. J. and Davis, R. A. (1991). Time Series: Theory and Methods, 2nd ed. Springer, New York.

Brockwell, P. J. and Davis, R. A. (2002). Introduction to Time Series and Forecasting, 2nd ed. Springer, New York.

Brockwell, P. J. and Marquardt, T. (2005). Lèvy-driven and fractionally integrated ARMA processes with continuous-time paramaters. Statistica Sinica 15 477–494.

Chan, K. S. (1990). Testing for threshold autoregression. Annals of Statistics 18 1886–1894.

Chan, N. H. (2002). Time Series: Applications to Finance. John Wiley & Sons, New York.

Chan, N. H. and Palma, W. (1998). State space modeling of long-memory processes. Annals of Statistics 26 719–740.

Chan, N. H. and Palma, W. (2006). Estimation of long-memory time series models: A survey of different likelihood-based methods. Advances in Econometrics 20 89–121.

Chatfield, C. (2003). The Analysis of Time Series: An Introduction, 6th ed. Chapman & Hall/CRC, Boca Raton.

Chen, W., Hurvich, C. M. and Lu, Y. (2006). On the correlation matrix of the discrete Fourier transform and the fast solution of large Toeplitz systems for long-memory time series. Journal of the American Statistical Association 101 812–822.

Chernick, M. R., Hsing, T. and McCormick, W. P. (1991). Calculating the extremal index for a class of stationary sequences. Advances in Applied Probability 23 835–850.

Chib, S., Nardari, F. and Shephard, N. (2006). Analysis of high dimensional multivariate stochastic volatility models. Journal of Econometrics 134 341–371.

Cryer, J. D. and Chan, K. S. (2008). Time Series Analysis: With Applications in R. Springer, New York.

Cui, Y. and Lund, R. (2009). A new look at time series of counts. Biometrika 96 781–792.

Davis, R. A., Dunsmuir, W. T. M. and Wang, Y. (1999). Modeling time series of count data. In Asymptotics, Nonparametrics and Time Series, ( S. Ghosh, ed.). Statistics Textbooks Monograph 63–113. Marcel Dekker, New York.

Davis, R. A., Dunsmuir, W. and Streett, S. B. (2003). Observation-driven models for Poisson counts. Biometrika 90 777–790.

Davis, R. A. and Resnick, S. I. (1996). Limit theory for bilinear processes with heavy-tailed noise. The Annals of Applied Probability 6 1191–1210.

Deistler, M. and Hannan, E. J. (1981). Some properties of the parameterization of ARMA systems with unknown order. Journal of Multivariate Analysis 11 474–484.

Dufour, J. M. and Jouini, T. (2005). Asymptotic distribution of a simple linear estimator for VARMA models in echelon form. Statistical Modeling and Analysis for Complex Data Problems 209–240.

Dunsmuir, W. and Hannan, E. J. (1976). Vector linear time series models. Advances in Applied Probability 8 339–364.

Durbin, J. and Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford University Press, Oxford.

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 987–1007.

Engle, R. F. (2002). Dynamic conditional correlation. Journal of Business and Economic Statistics 20 339–350.

Engle, R. F. and Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews 5 1–50.

Fuller, W. A. (1996). Introduction to Statistical Time Series, 2nd ed. John Wiley & Sons, New York.

Geweke, J. and Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis 4 221–238.

Gladyšhev, E. G. (1961). Periodically correlated random sequences. Soviet Math 2 385–388.

Granger, C. W. J. (1982). Acronyms in time series analysis (ATSA). Journal of Time Series Analysis 3 103–107.

Granger, C. W. J. and Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht Göttingen.

Granger, C. W. J. and Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis 1 15–29.

Gray, H. L., Zhang, N. F. and Woodward, W. A. (1989). On generalized fractional processes. Journal of Time Series Analysis 10 233–257.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press, Princeton, New Jersey.

Hannan, E. J. (1955). A test for singularities in Sydney rainfall. Australian Journal of Physics 8 289–297.

Hannan, E. J. (1969). The identification of vector mixed autoregressive-moving average system. Biometrika 56 223–225.

Hannan, E. J. (1970). Multiple Time Series. John Wiley & Sons, New York.

Hannan, E. J. (1976). The identification and parameterization of ARMAX and state space forms. Econometrica 44 713–723.

Hannan, E. J. (1979). The Statistical Theory of Linear Systems. In Developments in Statistics ( P. R. Krishnaiah, ed.) 83–121. Academic Press, New York.

Hannan, E. J. and Deistler, M. (1987). The Statistical Theory of Linear Systems. John Wiley & Sons, New York.

Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.

Haslett, J. and Raftery, A. E. (1989). Space-time modelling with long-memory dependence: Assessing Ireland’s wind power resource. Applied Statistics 38 1–50.

Hosking, J. R. M. (1981). Fractional differencing. Biometrika 68 165–176.

Hui, Y. V. and Li, W. K. (1995). On fractionally differenced periodic processes. Sankhyā: The Indian Journal of Statistics, Series B 57 19–31.

Jacobs, P. A. and Lewis, P. A. W. (1978a). Discrete time series generated by mixtures. I: Correlational and runs properties. Journal of the Royal Statistical Society. Series B (Methodological) 40 94–105.

Jacobs, P. A. and Lewis, P. A. W. (1978b). Discrete time series generated by mixtures II: Asymptotic properties. Journal of the Royal Statistical Society. Series B (Methodological) 40 222–228.

Jacobs, P. A. and Lewis, P. A. W. (1983). Stationary discrete autoregressive-moving average time series generated by mixtures. Journal of Time Series Analysis 4 19–36.

Jones, R. H. (1980). Maximum likelihood fitting of ARMA models to time series with missing observations. Technometrics 22 389–395.

Jones, R. H. and Brelsford, W. M. (1967). Time series with periodic structure. Biometrika 54 403–408.

Kedem, B. and Fokianos, K. (2002). Regression Models for Time Series Analysis. John Wiley & Sons, New Jersey.

Ko, K. and Vannucci, M. (2006). Bayesian wavelet-based methods for the detection of multiple changes of the long memory parameter. IEEE Transactions on Signal Processing 54 4461–4470.

Kohn, R. (1979). Asymptotic estimation and hypothesis testing results for vector linear time series models. Econometrica 47 1005–1030.

Kokoszka, P. S. and Taqqu, M. S. (1995). Fractional ARIMA with stable innovations. Stochastic Processes and their Applications 60 19–47.

Kokoszka, P. S. and Taqqu, M. S. (1996). Parameter estimation for infinite variance fractional ARIMA. Annals of Statistics 24 1880–1913.

Lawrance, A. J. and Lewis, P. A. W. (1980). The exponential autoregressive-moving average EARMA(p,q) process. Journal of the Royal Statistical Society. Series B (Methodological) 42 150–161.

Ling, S. and Li, W. K. (1997). On fractionally integrated autoregressive moving-average time series models with conditional heteroscedasticity. Journal of the American Statistical Association 92 1184–1194.

Liu, J. and Brockwell, P. J. (1988). On the general bilinear time series model. Journal of Applied Probability 25 553–564.

Lund, R. and Basawa, I. V. (2000). Recursive prediction and likelihood evaluation for periodic ARMA models. Journal of Time Series Analysis 21 75–93.

Lund, R., Shao, Q. and Basawa, I. (2006). Parsimonious periodic time series modeling. Australian & New Zealand Journal of Statistics 48 33–47.

Lütkepohl, H. (1991). Introduction to Multiple Time Series Analysis. Springer-Verlag, New York.

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer, New York.

MacDonald, I. L. and Zucchini, W. (1997). Hidden Markov and Other Models for Discrete-Valued Time Series. Chapman & Hall/CRC, Boca Raton.

Mann, H. B. and Wald, A. (1943). On the statistical treatment of linear stochastic difference equations. Econometrica 11 173–220.

Marriott, J., Ravishanker, N., Gelfand, A. and Pai, J. (1996). Bayesian analysis of ARMA processes: Complete sampling-based inference under exact likelihoods. In Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner ( D. Berry, K. Challoner and J. Geweke, eds.) 243–256. Wiley, New York.

McKenzie, E. (1988). Some ARMA models for dependent sequences of Poisson counts. Advances in Applied Probability 20 822–835.

Mikosch, T. and Starica, C. (2004). Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects. Review of Economics and Statistics 86 378–390.

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59 347–370.

Nelson, D. B. and Cao, C. Q. (1992). Inequality constraints in the univariate GARCH model. Journal of Business and Economic Statistics 10 229–235.

Ooms, M. and Franses, P. H. (2001). A seasonal periodic long memory model for monthly river flows. Environmental Modelling & Software 16 559–569.

Pagano, M. (1978). On periodic and multiple autoregressions. Annals of Statistics 6 1310–1317.

Pai, J. S. and Ravishanker, N. (1998). Bayesian analysis of autoregressive fractionally integrated moving-average processes. Journal of Time Series Analysis 19 99–112.

Palma, W. (2007). Long-Memory Time Series: Theory and Methods. John Wiley & Sons, New Jersey.

Palma, W. and Chan, N. H. (2005). Efficient estimation of seasonal long-range-dependent processes. Journal of Time Series Analysis 26 863–892.

Pfeifer, P. E. and Deutsch, S. J. (1980). A three-stage iterative procedure for space-time modeling. Technometrics 22 35–47.

Prado, R. and West, M. (2010). Time Series Modeling, Computation and Inference. Chapman & Hall/CRC, Boca Raton.

Quoreshi, A. M. M. S. (2008). A long memory count data time series model for financial application. Preprint.

R Development Core Team, (2010). R: A Language and Environment for Statistical Computing. http://www.R-project.org.

Ravishanker, N. and Ray, B. K. (1997). Bayesian analysis of vector ARMA models using Gibbs sampling. Journal of Forecasting 16 177–194.

Ravishanker, N. and Ray, B. K. (2002). Bayesian prediction for vector ARFIMA processes. International Journal of Forecasting 18 207–214.

Reinsel, G. C. (1997). Elements of Multivariate Time Series Analysis. Springer, New York.

Resnick, S. I. and Willekens, E. (1991). Moving averages with random coefficients and random coefficient autoregressive models. Communications in Statistics. Stochastic Models 7 511–525.

Rootzén, H. (1986). Extreme value theory for moving average processes. The Annals of Probability 14 612–652.

Scotto, M. G. (2007). Extremes for solutions to stochastic difference equations with regularly varying tails. REVSTAT–Statistical Journal 5 229–247.

Shao, Q. and Lund, R. (2004). Computation and characterization of autocorrelations and partial autocorrelations in periodic ARMA models. Journal of Time Series Analysis 25 359–372.

Shumway, R. H. and Stoffer, D. S. (2006). Time Series Analysis and its Applications: With R Examples, 2nd ed. Springer, New York.

Silvennoinen, A. and Teräsvirta, T. (2009). Multivariate GARCH models. In Handbook of Financial Time Series ( T. Andersen, R. Davis, J. Kreib, and T. Mikosch, eds.) Springer, New York.

Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics 53 165–188.

Startz, R. (2008). Binomial autoregressive moving average models with an application to U.S. recessions. Journal of Business and Economic Statistics 26 1–8.

Stramer, O., Tweedie, R. L. and Brockwell, P. J. (1996). Existence and stability of continuous time threshold ARMA processes. Statistica Sinica 6 715–732.

Subba Rao, T. (1981). On the theory of bilinear time series models. Journal of the Royal Statistical Society. Series B (Methodological) 43 244–255.

Tong, H. and Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data. Journal of the Royal Statistical Society. Series B (Methodological) 42 245–292.

Troutman, B. M. (1979). Some results in periodic autoregression. Biometrika 66 219–228.

Tsai, H. (2009). On continuous-time autoregressive fractionally integrated moving average processes. Bernoulli 15 178–194.

Tsai, H. and Chan, K. S. (2000). A note on the covariance structure of a continuous-time ARMA process. Statistica Sinica 10 989–998.

Tsai, H. and Chan, K. S. (2005). Maximum likelihood estimation of linear continuous time long memory processes with discrete time data. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 67 703–716.

Tsai, H. and Chan, K. S. (2008). A note on inequality constraints in the GARCH model. Econometric Theory 24 823–828.

Tsay, R. S. (1989). Parsimonious parameterization of vector autoregressive moving average models. Journal of Business and Economic Statistics 7 327–341.

Tunnicliffe-Wilson, G. (1979). Some efficient computational procedures for high order ARMA models. Journal of Statistical Computation and Simulation 8 301–309.

Ursu, E. and Duchesne, P. (2009). On modelling and diagnostic checking of vector periodic autoregressive time series models. Journal of Time Series Analysis 30 70–96.

Vecchia, A. V. (1985a). Maximum likelihood estimation for periodic autoregressive moving average models. Technometrics 27 375–384.

Vecchia, A. V. (1985b). Periodic autoregressive-moving average (PARMA) modeling with applications to water resources. Journal of the American Water Resources Association 21 721–730.

Vidakovic, B. (1999). Statistical Modeling by Wavelets. John Wiley & Sons, New York.

West, M. and Harrison, J. (1997). Bayesian Forecasting and Dynamic Models, 2nd ed. Springer, New York.

Wold, H. (1954). A Study in the Analysis of Stationary Time Series. Almquist & Wiksell, Stockholm.

Woodward, W. A., Cheng, Q. C. and Gray, H. L. (1998). A k-factor GARMA long-memory model. Journal of Time Series Analysis 19 485–504.

Zivot, E. and Wang, J. (2006). Modeling Financial Time Series with S-PLUS, 2nd ed. Springer, New York.

Harvested produce crops feed Florida Department of Corrections’ (FDC) more than 87,000 inmates; action saves food costs while reducing COVID-19 related supply chain impacts.

(PRWeb April 20, 2020)

Read the full story at https://www.prweb.com/releases/new_partnerships_emerge_for_covid_19_relief_dade_county_farm_bureau_teams_with_state_leaders_to_launch_farm_to_inmate_program/prweb17052045.htm

Hock Peng Chan.

Source: The Annals of Statistics, Volume 48, Number 1, 346--373.

Abstract:
Lai and Robbins ( Adv. in Appl. Math. 6 (1985) 4–22) and Lai ( Ann. Statist. 15 (1987) 1091–1114) provided efficient parametric solutions to the multi-armed bandit problem, showing that arm allocation via upper confidence bounds (UCB) achieves minimum regret. These bounds are constructed from the Kullback–Leibler information of the reward distributions, estimated from specified parametric families. In recent years, there has been renewed interest in the multi-armed bandit problem due to new applications in machine learning algorithms and data analytics. Nonparametric arm allocation procedures like $epsilon $-greedy, Boltzmann exploration and BESA were studied, and modified versions of the UCB procedure were also analyzed under nonparametric settings. However, unlike UCB these nonparametric procedures are not efficient under general parametric settings. In this paper, we propose efficient nonparametric procedures.

Steffen Grønneberg, Benjamin Holcblat.

Source: The Annals of Statistics, Volume 47, Number 6, 3216--3243.

Abstract:
We establish general and versatile results regarding the limit behavior of the partial-sum process of ARMAX residuals. Illustrations include ARMA with seasonal dummies, misspecified ARMAX models with autocorrelated errors, nonlinear ARMAX models, ARMA with a structural break, a wide range of ARMAX models with infinite-variance errors, weak GARCH models and the consistency of kernel estimation of the density of ARMAX errors. Our results identify the limit distributions, and provide a general algorithm to obtain pivot statistics for CUSUM tests.

Genetic genealogy -- Handbooks, manuals, etc.

The womenswear line is new, and there’s already a variety of items to shop.

Help raise the profile of the IYFF 2014 and its role in eradicating hunger Family farming is the predominant form of agriculture in the world with more than 500 million family farmers playing a key role in securing a future where more quality food is produced in a sustainable way.  For this reason, the 2014 International Year of Family Farming plays a [...]

Farmer field schools (FFS) are essentially schools without walls that introduce new technological innovations while building on indigenous knowledge. In FFS, farmers are the experts. Key features and principles of the FFS approach – TRUE or FALSE? The FFS approach allows farmers to learn through testing changes in a controlled, group-based environment TRUE:  Discovery-based learning is an essential part of the FFS as [...]

The United Nations launched the 2014 International Year of Family Farming to stress the vast potential family farmers have to eradicate hunger and preserve natural resources. In both developed and developing countries, more than 500 million, or nine out of ten, farms are managed by families, making family farms the predominant form of agriculture. They not only produce about 80% [...]

Maize, rice and wheat are fundamental to world food security. We must safeguard production in the world’s grain belts and rice bowls, and increase yields in countries where production has to substantially improve as populations grow. Climate change adds new pressures on cereals, including rising temperatures and a higher incidence of pests, diseases, droughts and floods. FAO’s model of ecosystem-based agriculture, [...]

Last year, Ashmita Thapa’s husband left their hometown in southern Nepal to find work in Saudi Arabia. He had been working as a farmer and used to be able to grow enough food for the family. But now, Ashmita explains, the yields are poorer and poorer. “This is a part of climate change,” she adds. “There isn’t as much rain as [...]

Munggah aq Amaq Genap, a 58-year-old farmer from Sekaroh Village in Indonesia, looks serious but content. He has the build of someone who has been a farmer for all his life. Amaq planted corn once a year. If there was rain, his harvest was good. If there wasn’t, his harvest was poor. But with the changes in climate, he was [...]

11 and 18 December 2019, Rome –A Farmer’s Market at FAO’s premises will take place on Wednesday, 11 December and on Wednesday 18 December 2019 from 12.00 [...]

The farmers will offer seasonal fresh fruits and vegetables to around 3000 people - including employees, contractors, delegates and visitors - that enter the FAO headquarters every day.

Centro Agroalimentare [...]

As of the start of the New Year, the Farmers’ Market will be back at FAO’s premises – Atrium - on January 29^th  from 12.00- 16.00 hours.

All of [...]

Buy fresh and seasonal produce at the Farmers’ Market on
Wednesday 26 February from 12.00 – 16.00 hours, and be sure to visit the [...]

The Farmers’ Market has been postponed for Friday 6 March and until further notice.

British Auxiliary Units were trained to sabotage the enemy in case of German invasion

The vineyard deploys a daily bird-based battalion to pluck snails and insects off their plants

Steeds who made headlines for winning races now get to enjoy their final years at a slower pace

The projects are helping Australia's drought-stricken rural towns find new life as outdoor art galleries

The water-floating farms in the Inle Lake, Myanmar.

It was a quiet morning at Nagarahole Tiger Reserve, Sun was opening up and the mist from the night was clearing. As we made a drive through the wood we saw this beautiful leopardess perching on her favourite tree. This leopardess was marking her presence by rubbing her scent glands to the tree trunk by hugging it. This behaviour was very interesting to witness as it was marking her scent on the top branches of the tree. This is one of the favourite pictures that I took recently because of the whole setup. Artistically one can visualize the tree branch as an "Arm of the Mother Nature" and leopard is holding on to it. It shows the unmitigated bonding shared between the tree and the Leopard.

The Beatles guitarist visited his sister in southern Illinois just months before he'd become world famous

The medieval English longbow first came to prominence during the Hundred Years War. In 1346, English forces used it to devastating effect to cut down a superior French army.

The government of the Northwest Territories has made an exemption on border restrictions for a veterinarian from Alberta to practice in the territory, after he received public and political support.

Some farmers across the province are worried about getting their fuel in time for spring seeding. The Agricultural Producers Association of Saskatchewan says it has been fielding complaints this week about delays at the Co-op cardlock near Moose Jaw.

Provincial Police have a suspect in custody after a firearms incident Thursday morning at the hospital in the small northwestern Ontario community of Rainy River.

This protocol describes the preparation of the shuttle vector before its introduction into bacterial artificial chromosome (BAC) host cells for BAC two-step engineering. The homology arm sequences, prepared previously, are introduced by ligation into the digested shuttle vector DNA to provide sites for recombination within the BAC clone. Crude lysates of individual bacterial transformants serve as templates in polymerase chain reaction (PCR) analysis to confirm the presence of the homology arms in the recombinant shuttle vector.

The 700-bp A homology arm (A-box) and the 700-bp B homology arm (B-box) are amplified by polymerase chain reaction (PCR) using purified bacterial artificial chromosome (BAC) DNA as template for two-step BAC engineering. The resulting A-box PCR product contains an AscI site at its 5' end (the 5' primer incorporates an AscI site, and the 3' primer does not incorporate any restriction sites). The B-box PCR product contains an XmaI site at its 3' end (the 5' primer does not incorporate any restriction sites, and the 3' primer incorporates an XmaI site). The amplification products are then digested with the appropriate restriction endonucleases to render them suitable for cloning into the shuttle vector.

The Metuchen Farmers Market, like many others, has moved to online orders and drive-thru pickups during the coronavirus pandemic

1. ‘Army won’t be deployed in Mumbai, will fight Covid-19 together’: Uddhav Thackeray  Hindustan Times
2. Maharashtra may extend lockdown to end of May, hints CM Uddhav Thackeray  Times of India
3. Maharashtra CM Uddhav Thackeray announces compensation for kin of deceased  TIMES NOW
4. Restrict entry-exit of migrants in Maharashtra: Raj Thackeray  Deccan Chronicle
5. Uddhav Thackeray: You are the soldiers, no need for the Army  Mumbai Mirror
6. View Full coverage on Google News

1. Salman Khan shoots song titled 'Tere Bina' with Jacqueline Fernandez at his Panvel farmhouse; reveals det  NewsEverything
2. Salman Khan and Jacqueline Fernandez get candid about upcoming song Tere Bina; Watch VIDEO  PINKVILLA
3. Salman Khan’s ladylove Iulia Vantur feels he looks like a “Supermodel” in his latest picture with Niketan  Times of India
4. Iulia Vantur asked when she is marrying Salman Khan, says ‘spending your life with someone’ more important...  Hindustan Times
5. Salman Khan's caravan Being Haangryy provides essentials to needy people of Mumbai  Bollywood Life
6. View Full coverage on Google News

Wellington OPP say three people made off with electric toothbrushes worth more than $700 from a pharmacy in Erin, Ont., on Wednesday.

As a farmer, spring means a fresh start, a chance for new life, hope and opportunities.

A New Zealand mail carrier made it her mission to deliver a package that was vaguely addressed to "Phil and Kay," located "opposite Cust pub or thereabouts."

When Antje Springman spotted something huddled outside her home, she thought it was one of her chickens. It turned out to be a Great Cormorant.

Interior Health officials are asking people who went to the Save-On-Foods pharmacy in Columbia Place Shopping Centre in Kamloops, March 10, 13 and 14 along with March 16 to 21 to self-isolate following a positive case of COVID-19 at the store.

Bryan Reid is known for building custom log homes on his HGTV reality show Timber Kings, and he’s also in the business of pulp — cedar pulp used to make medical garments,an effort critical during the COVID-19 pandemic.

Farmers markets throughout B.C.’s Interior and South Coast are ramping up for their spring seasons, but COVID-19 has forced them to make some changes to how they operate. 

A 23-year-old man has been granted bail, after being charged in the death of a Saskatchewan farmer with a wife and two kids whose body was found dumped north of Calgary.

The hoarders, the deliverers and what the coronavirus says about class in America.

Allegations about Valeant’s practices and its own disclosures while under pressure cause one to wonder.